{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "data": {
      "image/png": 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vp73dNtRNUShGJEpjCn8GxMg1NTX5toXPy8sjPz/f99xut0dcz7PPPkttbe1ANHHIWL16H7fc8harV+8LOP7jH/+YoqIiioqK+Oc//+k7vmLFCqZPn05RURE33nhj0Ozwx44do6ysjNLSUoqKinjqqacG/H0ohhalsdCE0th//dd/MWvWLF9mFG9KwxdeeIHi4mJmzZrFXXfdFbTOv/3tb77Pt7S0FCEE+/btC1pWEWeE2mgu2F9/N3SMFu+mjkOJw+GIaX1tbVb55JPbZFub1XdszZo1cunSpdLpdEqz2SzLysqk2WyWUkq5du1a6Xa7pcvlkldffbX885//3KNOq9UqrVarp/42WVBQIOvq6mLabkXfYRA3TY2W00VjH330kTzvvPOky+WSDodDzps3T3788ceyrq5OTpgwQTY2Nkq32y2vvfZauXHjxrD179ixQ06dOjWmbVb0j3AaG/Tpyr/+9a/Mnz+f0tJSvv/97+N2u3E6nXzrW9+iuLiYoqIiHn74YV566SV27drFN77xjaC90yeeeMKXaf/rX/86FosFgNraWpYvX+7LiL5lyxYAnnvuOd8xb5by//iP/2DNmjW+OlNTUwFtC5slS5awYsUKzjrrLAAuv/xy5syZw6xZs3j66ad956xdu5aysjJfpnWXy8UZZ5xBc3MzAC6Xi8mTJ/uep6cncPPNc0hPT/DVceDAARYvXoxeryc1NZWioiLWr18PwKWXXooQAp1Ox/z586mqqurxmSYkJJCQoNVns9lwu92+Xqri9ENprKfGhBBYrVbsdjs2mw2n08mYMWMoLy9nxowZZGVlIYRgyZIlvm19QqG21xlmhLJ+wf7628vcu3evXL58ua/ndtNNN8nnn39ebt68WS5btsx3TktLi5QyfC+zsbHR9/9Pf/pT+ac//UlKKeVVV10lH3nkESml1kNsa2uTu3btktOnT5dNTU1SSul7vO666+Rrr73mqyclJUVKKeV7770nU1JS5PHjx32vec/p7OyUM2bMkM3NzbKmpkYWFBTIioqKgDJ33323rw1r166V11xzjZRSyldffVXef//9Pd7L2rVr5XnnnSe7urpkfX29nDhxonzooYcCythsNllSUiI3bdoU9PM4duyYLC4ulklJSfLxxx8PWkYxNDCIIzmlseAak1LKO+64Q2ZkZMj09HR5zz33SCmlbGhokPn5+fL48ePSbrfL5cuXyyuuuCL4By2ldLvdcuLEifLgwYMhyygGn3AaG9QEzRs2bOCzzz5j7lxtRwSLxUJBQQFLly7l8OHD3HHHHVx66aVcfPHFvda1Z88e7rnnHlpbWzGbzVx22WUAbNy4kdWrVwPaLsTp6el88MEHfOMb32D06NEAvsdwLFy4kAkTJvieP/jgg7zxxhuAtilpeXk5lZWVXHDBBUycODGg3htuuIGvf/3r/OAHP+DZZ5/lxhtvBODKK6/kyiuv7HGtSy+9lG3btrFw4ULGjBnDwoULMRgCv5pbb72VJUuWsHDhwqDtLSwsZM+ePVRXV3PFFVdw9dVXq92KT0OUxoJr7PDhw5SXl1NdXY3L5WLJkiUsXbqUc845h8cee4yrr74ag8HAggULgs6WeNm0aROjRo3izDPP7PX9KeKDQZ2ulFJy/fXXs2vXLnbt2sXhw4f5+c9/TlZWFnv27GHRokU8/PDD3HLLLb3W9e1vf5vHH3+cvXv3cvfdd/t2wgZtaqL7dbsfA02gbrcb0KY8/IM6UlJSfP9v2LCBf/3rX2zevJndu3cze/ZsrFZryHoLCwsZNWoUH374ITt37ozoB+Wee+5h165drF+/HpfLxdSpU32v/fznP6etrY3f//73vdaTn5/PmWee6ds9XHF6oTQWnFdffZVzzjmHlJQU0tPTWbZsGZs3bwZg+fLlbN26lU2bNjF16tQA7XVn9erVaqpymDGoRm7JkiW8/PLLNDY2AlqE2IkTJ2hoaEBKyde//nXuv/9+duzYAUBaWhpmszloXZ2dneTl5eFwOHjhhRd8xy+44AKeeOIJQBNVe3s7S5YsYfXq1b45e+9jYWEh27dvB+C1117D5XIFvVZbWxujR48mKSmJ/fv389lnnwFw7rnn8sEHH3D8+PGAekHraV533XWsWLECnS78x+x0On3n7ty5k4MHD3LRRRcBml9k48aNPP/88yHrqaqq8v0ANTU18emnnzJt2rSw11SMTJTGgjNhwgQ++ugjnE4nDoeDjz76iBkzZgBQX1/vq/uJJ57ghhtuCFqHy+Xin//8JytWrAh7LUWcEWoeM9hfLCK/nn/+eVlSUiKLi4tlWVmZ3Lp1q9y+fbssLS2VJSUlsrS0VK5bt05KKeVLL70kp02bJktKSqTNZguo95FHHpGTJk2S559/vrztttvkDTfcIKWUsqamRl522WWyqKhIlpaWyi1btkgppXzmmWfkrFmzZElJibz++uullFKePHlSzps3T86bN0/+z//8T4C/YPny5b5rWSwWefHFF8vZs2fLa665Rp533nny448/llJK+dZbb8mSkhI5e/bsAJ+HzWaTycnJ8vPPP/cdC+Uv6OjokDNmzJAzZsyQZ599tty9e7eUUkqn0yn1er2cMmWKLCkpkSUlJfJXv/qVlFLKzZs3y1tuuUVKKeU777wji4qK5OzZs2VxcbF8+umno/6eFAMHgxxdqTTWU2NOp1PeeOONPp39+Mc/9r129dVX+46/9NJLIet677335LnnnhvRd6IYXMJpTMgoovDmzp0rt23bNmAGdySxefNm7rrrLj788MOhbopiiBFCbJdSzo2krNJY5CiNKbyE05jaGXwA+PWvf82f//xnn3NeoVDEFqUxRaSokVyMaG+3sXr1PlasKApYn6NQqJFc/1H6UoQjnMZU7soYESqVkEKh6D9KX4q+oqYrY8SKFUUBjwqFInYofSn6ijJyMcKbSkihUMQepS9FX1HTlQqFQqEYsSgj50HtQaVQDCxKY4qhQBk5D8qxrVAMLEpjiqFA+eQ8KMe2QjGwKI0phgJl5Dwox7ZCMbAojSmGAmXkFApF1Ljdkvr6Tqqq2qmpMWOxOLBaXej1goQEPampCYwfn8748elkZCQE3UlAoRgMlJGLA1Q2B8VwoLq6nT176ti3r56jR5txOLQtdIQAvV6HTieQUuJ2S5xON0IIpISMjARmzMhm1qwcSkrySEsb/Htcaez0RRm5OMDrkAfUdI4irpBS8vnnTbz99hF2765Dp4PU1ARyclIwGHqPW5NSYrO52L27jk8/rcJo1HHRRZO58MJJjBmT0uv5sUJp7PRFGbkokN02cOz+vK8oh7wi3nC7JTt2nOT11w9z4kQbKSkmJk7MiPp+F0KQmGggMVH7qXE4XLz3Xjnr1h1l/vx8Lr10KhMnZvrKK40pYo1K0Bwh9923kdZWKw8+uNQzDSNZuXIdmZmJ3Hff4qFuniKOGW4JmltbrTz77E527qwlKytpQHxqbrekrq4Dm83FlVeeyVe+Mo1f/epfSmOKPqESNPcTKSWtrVZWrdrCypXrfOJbtWoLra1WoukoKBTxzIEDDdx99wccPNjA5MmZZGYmDkjQiE4nGDs2jfz8NF555SC/+90n1NSYlcYUMUeN5CLEX3Re7rhjga/XqVCEYjiM5BwOF6+/fpg33jhMdnbyoAZnSCmpre0AvAEi+32vKY0pIkGN5GKAEIIHH1wacEyJTzESsNtdPProVt544zATJmQMevShENqoLj09oUcwi9KYor+MuMATt1vS0NBJTU0HFRWtHDrUSEuLBbvdBYDJpGfUqCSmT8+isDCTcePSyMlJQacLLyTvSM6flSvXKREqhjUOh4snntjG7t11TJqUOaT3cnKykUOHGgOOKY0p+suIMXKtrVa2bKninXeO+hLACgFpaQkkJOh90V1utzY1cuRIky9yKzXVxLJlZ7BwYQGZmYk96vafqvROn/hPXSoRKoYjbrfkr3/dxfbtJyksHFoDJ6Vk3bpytm2rYe7csZx5ZjYOh0tpTNFvhr2Rq6xs4513jrJ5cxVSQk5OMhMmZIQ9JynJGGDMurocvPzyfv7xjwO+sGb/OoQQZGYmBvgHvFOXA+WYVygGmrfe+pyPPjrOpEmjhvwe9i41WLAgn6VLp2C1Oqmv7+Q73ylRGlP0i2EbeOJwuHj33aO8+upBjEY9ubkp6PWRuRgbGjpZs+YwV1wxnZycUwtSXS439fWd2O0urrjiTC65ZComk973eizW8KjMC3GOrR0Or4bpKyAhvf/liM/Ak/376/nd7/5NQUE6RqO+9xOiJJTGesNfU21tVhwON7/97RJSU00R16E0FucMssaGZeDJiRNt/PKX/+KVVw4yblwa48alRWzgANasOczJk2bWrDkccFyv1zF2rFbfa68d5Be/+Ijjx1t9r3c3aH3pXartRuKcw6vhvVu0x1iUi0NsNid/+csuRo9OGhADB6E11hv+msrISKSry8GaNYeiqkNpLM4ZZI0Nu+nKXbtqeOSRz0hM1FNYmNn7CUG44orpvl5mMIxGPYWFo2hs7OL++z/ittvmMWfOuP4024fKvBDnTF8R+NjfcnHI+vXlNDR09Vk/kdCbxiIlPz+N994r59xzC5g0aVRE5yiNxTmDrLFhNV25ZUsVf/rTZ+TmppKcbByUa3Z1Oait7eB735vL2WePH5CUQ4qRTTxNV9bVdfCzn71Pbm5qwFR8vBDMJdDUZCErK4l77jk/qhkbxenDiJiu3L27lj/9aRt5eYNn4EALax43Lo1bb32L6657xZd5wRtxed99GwetLQpFf5BS8ve/78Fg0MWlgdu4sYJ168oDNLZuXTl799ZRUdHKxx+fGOIWKoYjw8LI1dZ28OijWxkzJpmkpMEzcF4SEvQYDDpefHE/N930pko5pBiWVFS0sndvPXl5qUPdlB5IKbFanWzZUu0zdOvWlbNlSzU2m4vc3BReeeUATqd7qJuqGGbEvU/O5XLz7LM70et1pKREHmEVDpvNyb599RQVjSEhIfRH4F/ussum4XC4eeaZnTzzzE5ApRzqFSlBusDtAp0BhE5bvKgYEv7970oMBt2g3K990djSpVMA2LKlmi1bqgF8SwqEEDQ0dHH4cCOzZo0Z8PYrRg5xb+Q+/PAYhw41MmlS7Jzk+/bV89ZbRwDCBpR0L3fFFdPZu7fe97oycB6kG7oaoOMktFdA8yFoPwEuK0hAcOpRnwgZE2H0DEgvhNSxkJStGUDFgGG1Ovn44+ODtodbXzW2dOkUn4EDfAYOIDHRwMaNFcrIKaIiro1cQ0Mnq1fvJz8/rU/GJNS6tqIiTSTex1D4l5NSsn79FwGvn9Yph1wOaNwHJ96Hls/B7dSOCwGmNEjKAqEPHLlJqZXrqIGWI9pzAJ0RsmbAhAsha6Y26vNHyp71nI6feT/Yv78eu90VU19cuHWjfdXYunXlAa+vW1fuM3TZ2cns2FFDe7tNrX+LNSNYY3Ft5DZurEBKGXa6I9y5VqvTJxCvgBITDSxeXBjRkoCEBANz5owL8A8sWJDPrFk5nDxpPj1TDlma4OSnUPEu2DvAlArJY3oapmAIAXoj6EdBol84uNupGb2G3WBKh0nLYOxCSMyETfeBrRUWP6idLyVsXAkJmXDOfQP1Lkcc779/LGbT/dC7vrza6Y1QGlu6dIrvOWgjOr1e59nMtYbFiwtj9l5Oe0a4xuLWyFmtTt5//xi5udE7yf2d2ECAYBYsyI869L97yiGHw41er+O22+adPimH2k9A+RtQt10TQlKONs0YC3QGSM7R/nd0weF/wOf/gNx5YK6Cfc9ory1+UBPfjlVQdseI6m0OJGazjYMHGykoCJ81IlJirS/oqTEhhM9Hl5ho8NWXkZHIxx+fUEYuVkipGbgdq7TnI1BjcWXk/NPxHDhQj9XqjHh6pbujuzcndrQsXlzoE6/JpMfhcPHtb5ewaNGEqOsaVrgccOwdOPIaGBIgrWBg/WfGZDBO0IJV6ndq/r4pyzXReYVYdsepXqeiV2prOxBC26g00oCQYEQTJNIX/DUG+Aydf31paSZOnGjF5XKrNXOxQAhNSzBiNRZXd4l/Op51674IuiNAKLwO7H37tMAQ/56gl1N8gPEAACAASURBVP4I0Funl8zMRNavLw9TegTQfgI+/QUceRVSx0FK3uAFiOj0p65p6jYCGSHiGyxqasy+ZS7ddRIN/ucOhL6g99R53inLpiZLv66j8MPf0HkZQRqLKyO3YkURTz55GVdeeSYVFa1kZETuXNbC/Kf6HNmhnNjd17T1dY1benoClZXtWK3OPp0f17gccPQN+Pe9YGuBjELNlzYU6IzQ3C3/4Yd3nApaUfTKF1+0+kZt3XUSDf7nhtNXrDQWCim1zC2KGOH1wfmzceWI0VhcTVempydw881zOH68FSGiS4Ds7+gO58SuqGjlllvmBHWWR4MQAiG0XnKkOfWGBQ4L7HoMGvdC6vihM26giaxiHdRthbwFUHABlK+BnY9o05gXPjJiepsDydGjzb4s/pEGhAQjkiCRqqp28vPTWLbsjH5rLBxVVe0UF+fGrL7TFq+B8/rg/H1yMCJGdHE1kvNy8qS5X72/UE7s3NwU6uo6e2RUsFqdfbqe2y2prm7vczvjDnsHbH8Amg5oa9iG0sCBJi5DombgCpdqPsGpX4fRZ2rLFpzWoW3fMMDhcFFTY45pKrxQ+po/Px+ArVtPxkxjwUhONnLkSHNM6jrtEUKLovT3wS1+UHuekDnsDRzE2UjOS3l5S7/X83id2GaznQ0bvmDJksnccsscn+i6O8vtdlfUDvnERANHj7awaNHE4b+HlaMTtv0RzJWe4JI4ubkLFgdGeOl0mqHrqNJ6m3N+qBlCRVC6uhxIqQWdxJJg+lq2TPPRxVJjXvyDXhITDTQ3K59czDjnvkCNeQ1dvPwG9JO4HMm1tVljsmhVCMGGDV+wd289GzZ8EdZZ3heHvNGoo73dBgzzPaycVtjxCJhPQNr4+Lu5u7dHp9MMccth2P2E5kNUBGUgcz0G01esNebF/1ydTmC3u2L1NhTQU2Px9hvQD+JmJOcfOmyzhb6Bo9mdW0rJkiWTAViyZHLYjAqRZmjwR6/XYbNpgSfDdg8rKWHfc5rBSBtGyyGE0NpbvxMOvQAzvz2ihBkrnE63Tx+Raieact31BcRUY178z3W7JQ6HMnKKyIgLI3fffRtpbbX6MofodPDxxyfIzEwMcFb3lmXBH/+yV101AyklTz65nbq6zpAZFaJ1yEspfdNA3qCZYUftZ3ByE2RMHn5GQghInwjH34cxZZBTPNQtiju0+1NGrJ2+lPPq6913y6mubqe62hwzjXnxD5ixWBzDPwGDlNoMinRqQVTeP/AkMvf+6cGQNPy0GUcMuZGTUtLaag1IkfXGG4fZvbsuIHtCNFkWQpWtq+skNzclbEaFaHC7ZVzuyxUx1lZtFJeSN3xFJHRa5pW9T8OiX2tpxhQ+DAYdUhKRdiLVWKhyW7dWk5+fxvz542KmsWC43X1L9TdoSAm2NrA0aMFcDjN0NYK1ESyNYG3RtCedaFnLu+FNraVVpt3jpkwtzV1StpYXNilHyxFrSoPELE+u2GGq4QFmyO8UIQQPPrgUgFWrtviM3cyZ2QGLS/0F01uWhd7K+jvAvedGmgmivd3mc7RbLE7Gjh3cH9VOt4sPOlu5MCWTFF0YA2trh8Orta3jE9J7HjOlwcG/g9sBxsHJTD9gJKRrATOHX4ai7yqx+2EwaG73SLQTqcZ6KwfETGPBgrhcroHrXEasL9D0dOhFmPwVzZiZK7Xp84p3IWWstuMGUhuh6QygN4E+AXQmrWPZW/1epBtcds04dtaC267pFt0pg6hPgFFTPLt7TISUcVp+WKWFoTdycMrQeQ0cwIIF44NmPwi3FUekZYNtAxLp1iBeR7tWbuygr5H7oLOVB5qrALg8LSt0wcOr4b1btP9n39zzWE4p1GyFjEkD2NpBJDUfKjdC3jw1belHSooJo1GPyyUj0k6kGuutXKw0dtVVM3q8brE4+uTXi4SI9GVr15bZ7HxYM3LjFsKoMwEJbRVQtREmXwa5U2PTKKHTIojDRRG7HFqGosYDp44ZkiDzDBhTCtnFkByjXLPDjLgwct6dtv3ZsqWKgoL0Hg7wcFtxdK8zGgd4pE5xf0d7a6uVcePSInmLMePClMyAx5BMXxHwKN1uhPfYlOXITfcjUnJHTk/PN235DJz/h6Ff4xcn6HSCiRMzqKvrCDBIEFw7kWqst3Kx0lgwrFYnU6YMTOcypL6srdC0H6o+1oK0AAwp2vrN7FIwJmrTucl52pR5VlGfElX3Gb0R9KMhcfSpYy47tH0BDXsAqY3wxp/nMXhjRo72e2HIjZzXwK1atcW30/Ydd7zLI49s5Z13jnLJJYGZE8JtxeHvk+utbPeeZKSZINLTE7jqqhm4XG7MZtugbULpJUWnDz+C85KQ7hvB7frgx2BrpWTpnxGzb0ZWfkzz8fdwJOWQN+niAW7xIJKQrvWkG/dB7llD3Zq44YwzRvHCC3vZv78hrHYi1Vgk5YLpKVqNhUIIwdixA9O5DNCXrU3b/qnqY2g9qh0zpgUmKU/TFsDXHlsPTiu5Z1yOyJ2DlJK6o2+CIXHoNKY3efx3WdqUpr0dDj6v/Z+WD/lf0nSSPLI3oR1yIyeEIDMz0WfghBCsWrWMffvqaW+3BfgBItmKI9qyfaWxsYvZs/PiPhO6dLvB1krpgWfYBZRc/CR17/8/8tqOUmtMG9ze5mBgSoNjb2tTNCPpffWDSZNGYTDoetVDpLoZDH31Rm7uAHYuO2uhYj1UfQRut9Z5SpsQ8n6SnkjJvMad1AK5Z1xO3dE3tefZZ8WHxoSAhAztT0qwm+Hzl+DQasibC5Mu0VwXQ93OAUBEk2pn7ty5ctu2bQPSkO43wuHDjfzmN59QWJgZtlxv6+T8HeAmkz4qB3g4KipaufPORZx5ZvzPc0u3m93rbqb0wDO+Y7VZpeRO/Wr/xWdrhxMbYMKSUwEuQ4mU0H5ci7T09LKHGiHEdinl3EjKDoTGTpxo4/77NzJ+fM/p//6ukwumL+i59VWssNtdtLfbeOSRS2JvODpOwtE1mq9aZ/AEh0TWdu/ILa9xp+9YbfZZ2sgunjUm3dBVpy1nGH0mTLta8+MNM2MXTmNDPpLz0v1GmDo1i9zcFMxmG2lpCSHLhbuB+htkEoqODjvZ2clMmxbBtGEESCnplG6aXA4anA4qHTba3U4cUuJCogcMQkeS0DHemECuwUSW3kCGzoAugptR6HSULP0z+Bm5mBg40MTXuFf7f+pV/a+vvwih/TBV/QtmfHOoWxMX5OWlotfrcDrdGI2nIvpCff+RaiyUvqD/GgtFa6uVmTNzYmvguhqh/HVtWlJvgvQJUW8pJYQg94zLwc/IxcTAwcBqTOi0SFAptQ2KN/8Kcko0Y5c+jJJDhCFujFx3dDrBpZdO5bnndgUYuWjpjwM8FI2NnXznO6X9ygfokpITDht7bB2ccNjodLsQaAt3E4UOoxAIzxEnYJcuzDg54bDiTdRkEIKxehPFiSlMMyWToAsuTN9Izu9Y3dE3YyPCCUsCH+OB5Fyo/BDOWK5twnqaYzLpOffcAj755AT5+bEdCYTSUn81FoquLgdf+tLE2FQmJdRsgX3PaiOatILIw/p7VOUZyfkdG1YaEwKSc0Bma8nPN90LU6+GScv6/JnEC3Fr5ADmz8/nrbc+p6XFwqhRSX2a2+6PAzwYra1WsrKSOfvs8X06v9Pt4pCti82WdsxuFyYhSNXpSdUbI3pvaX52zCUljS4HazuaWSdaKEtMZXZCKtmGU5GF/lOVhyctZ1pSJnX1ewL8Bz3SPnXf8r77c38S0vvcu4xm6jkq9EZwO7XIsuxhlmZtgDj33Als3FjR43h/v4NQWuqPxkJhszlJSTEyY0YMXAQOi5YOrnKjNpLpR2fIf6rSO0Xpe84w05gQ2jSty6H57JoPQvEN2pq7YUrcRk3cd99G7rrrfW68sYzWVitOp4t168qDCnWwcLnctLRYuPnmOSQlRReibnY5ecfcxGPNJ9nQ2YIOQZ7BxGi9EZPQ9enHXS8E6XoDeQYTGToD2y0dPN1aw/NtdVQ7tMTRQqeDhEx2zbyBaTOvRegM5J5xOceySmgXp/o4Uko+s5q1KLGKdacyLnj3dKvcGHX7wrHL2sFn1lNbKnmvv8saw80w2ytjV9cwZ/LkUWRlJdPRYfcd27ixImAjYW/U5FBqLBz19Z0sXlwYMOXaJ9pPwKf3QfUnWrBFP0f7QggwJAb44Ia9xvRGSJ+kjer+/XNoGIaJ5z3EpZHzT/X1+OPbWLbsDF599VDM96WKlurqdi6+eArTp0fek5RScsDaydOtNeyzdZGlN5BrMJEUYmqxrxiFIMdgJFdvpMHp4G9tdWzsbMUu3ZRe+H/a8oGWw1r0IVA//gI2ZZf5RPCZ1cwBW5fmgK7dckqEFeu0505rzHYKllJil24O2Lp6XN8u3bH5fo0pWi9UAWjT/1/+8mSamrqAwNR3A7n3W6yQUuJySRYuLOhfRSc3w6b7tK2l0idG7XsLRd6ki3tMTQ57jQkBqeO0bCrbfg9HXh+Wu4UP+HRlNPus+Zf1T/Xlpbg4J2SGk1DEKsqrtraDMWNSw67f6Y7Z5eS9zhYO2SyM1htIjGK5gV26OWa3MsmUiCkKIQohyNQbSJOSzZZ2Dtu6uCwti3y9XttKJzkPIQTzk7RIuwO2Lk14wMyEZM15bjBpoqv1fPbeTUsj/dydNmjaB1lF2kan3Y4JQwLzEjVj2/368xLTgn+/weoMhykNWsvDTwONECLV2Lx5+Tz//F62bq2ipCQv4jR54RioKMruNDdbKCzMJD+/H+vjGvZpWzOl5IEhsc8aC0X3ZUwjRmOmNC17ypF/agvdJ14U3QczxAz4SC6afdb8y/rntPRy0UWTaWjoiur6/dnDyktDQyeJiQZ+/ONzIp6mPGLr4unWGsrtVsYajCRGOXI7Zrey2WrmmL1vu1/rhTYdapNu/tZWx+bGI7jdLp8TWQjhE4EX381fGPi5RyU+0ITyxVvaY4hjYa8faZ3h0JvA2aXl+xvhRKqx0aOTSE428s475ezbVx+wvs1LtJ3IWOirN1wuN21tNq67rrjvPtv249omu0nZvvRYx+xWPrW091ljvTGiNKYzaIE5B/4KdTsib2ccMOAjuUj3WZNSBpR1u9386EfrA8p0dDhISzNRW9tBXl5qwLmhnKv9jfKqq+sgKcnIf//3uWRnRzZ3v9vawdsdTWTqjCT1cbH4JFNiwGNfSdcbSJGS/Y3ljHNYGS8lOk/Wis+s5oCyn1nNmggqAlOsUbEuOhFmFQU8SikRfse8UyUhrx/sOt3qjAwBnTWQNLr3osOYaDT2i19cwNGjzUycmBF1mrxgGhuoKEp/Tp40c8EFhUyd2sclO9ZWbdd7Y1LALhWx0lgoRpzG9CZIzoOdj8E59w6bJQZxsRi8+35ybrebOXOeYteuWl8mFG/qr5tvLmPMmBQqK9vJz0/j3/+ujHiPuWhwOt1UVbWTn5/Oj350NllZkRm4bZZ21ne0kG0wxmQKJFaMOflvxn/+IsaMyUwzJbHdMz/vnb7wztcva9iqLWj1Tp94/QXRTqd42GXtwC7dPmFJKdlqNdPodNDgcvS4ftjplGhpOw4lt8C4s/tfVz8Y6sXgEKixPXvq+L//28T69eXU13f1SM3Vfcoymn0cY01XlwOz2cbvfvflXt0dIdnzFNRs1kYig4S/D2zEaayrQVtucPbP42Z5QVwvBg+2n9yPfrSeXbtqKS3N44EHLg6YuszMTORnPzuPtWs/5/XXD9PSYmHPHm2qJNwec9HQ1NSF2Wznq1+dzmWXTYt4W4891g7Wd7SQYzBhjDM/kMFtJ1HoqXPa0QFGRMDN7pvWMCQGis07rWJIjFp8/s5vwCe0g7YucvRGZph6Xr+vkaZBEWhLCU5zumvsgQcu5sSJdurru3rdXzGafRwHot11dR3ceGNZ3w1cyxEtijI9RmvrIkQIgUnoRqbGkrK15TnV/4aCL/W9nkEiLkZy/kmavdxxxwIeeOBidH6+LK+gvI72hQvH8+KLe1mz5hAHDzb5ykXrPPfW3dJipb3dxtixadx8c1lU2+gctXXxD3MDWfr4GsF5Kah4l4LK9XQmj6VDuhhnSGCyMRHhsvuczVJvinoNT5e9i+ra7eTnzSHZ1HO0GxBV5sErfAh01sf8B7OtAmZ+GyZeGLs6+0A8jOSCaay4eAwXXTSJjIzEgHLdU9+ZTPqABMzQN41FS3V1O1OmjOYnPzmn7zlidz6ibT+TkhvbxkWIlHJkasxu1nYt/9JvYxah2h/CaWzoW0fgxqleHnxwaYCB85aDU472Tz+t4v77L+Dvfw9cKLloUWTTElJKOjvtVFW1c/x4G7m5Kdx++wJ+8YvFURm4DreLNzuaydQZ4tLA+SOEIFXoOem00ep2BjibfTd/dxGEEUV17XamVn9Ade32kNcL5fyOJkWbon8E09jLL19NS4sVq9UZUA4CA0piEaASLQ0NnYwencT3vz+vTwbOJSXtnQ101myj0ZRJtcPGUZuFw7YuDtq6OGDr5ICtk0O2Lj63dXHCYaXBaafN5cTiduGKUai8EGJkasyUpu183nI0NvUNIEM+XQnB95NbuXKdz0fXvay/o12vF/z1r3sCymzceJzi4jG++WmdTvhScLndErdb+l4bMyaFJUsmsWjRRPLzo5+nllKyoaMFp5Qk6WPzcQ5EJhCXIQEhtYRgQggS0PG53ULZ6FkYIdDZHEUvMz9vDkc8j8HKhnW+B6szmkwQvSGE5iwPRiyvMwwIprEnntjOTTeV8cQT2ygoyPBNy0spAwJKpJS8+25ggMq775azbNnAGLrmZgsgWLlyYcTTlHbpps7p4ITDyuf2LuqdDsY07WOO3UK7SYue1IMnUR6ARCCQgARcTgkCT2I9jQQhGK03MkpvIFWn73sHNlhAx4jQmIDWIzB6WvCX40RjQ27kgu0n5z+t4m/o/J3nN988B7fbTX7+A9TWdnL77Qt46KGl/PCH63j44S3odILVq7/GyZNmWltt2GxabzUhQU9mZiLjxqUzdmxq1JlLunPYrvUKxxpC/JhGSTAn8mdWMyahozQxtfcKQuDWJwQs5DQJHR1uFxVScsaYslNiqNyoLUr1+gu8i1UNiVCwuEe9yaZkpk44L+i53nRHJnTMHP+lAOc3BAlnjvLavSIJnkV+031ga4XFD566zsaVkJAJ59wX/XXinN409p//Wcpf/rKLgoIMNm06Fcg1Z844pJT88Y+f0tnpYP78fJYtm8K775azdWs11dXt3HDDWTE1dC0tFpxON3feuSgggjoYnW4Xh21dHLZbqHScWgaQotOTozcy0VJDot6Au1twRK3TjktKxhlMPo2ddDl8y268n5kLqHPaqXFqWWKShY4svZEcg5HkaAIuDAmQO+fU85GiMWMqNB+CyV/p+VocaWzIjZwQPfeT8w8y8XeAdw9QWblyPbW1nZ7XvWmLtR9ynU4wadIoJk8euPDxDreLdzpaGBVh3sne8DqRD9otgJ8T2W5hhqlvuTu9OIwpPXpRKUJHrdNGtt7AKL1RuxG92RigZ+RXuJ5YkHNFxTotiiz7LHJ7c37359qhEMKX4SXgOrZWbc0UaCLcuFJ7XnbHiBzR9aaxiy7SduD+299209ZmZdeuOkCbknz33XI6Ox2emmS3x9ghpeTkSTOJiQb++7/PZcKEjJDl6l0Odlo72GvtxI0kWacjW2/ssSNHoqURlz6hx/kuKWlwae9pnMHESaedBpeDHL3RpzEhBAbAIE6Nbp1Iqpw2Kp02MnQGxhtNEe8E4teAkaMxQ6K2g0OwdsaRxmIaeBIs80Kk2RgimaIL5jy//fYFSOnmkUc+8zs2n4ceWhYQpBJJxpVI6HS7+KCzlQtTMtnU1cZOaydjDMFHg51uF9stZuYkpZHi6fn1lmXBO3LzGjqAGaakUz2yIFkJ7A4LzfW7GD2mFJMxKWhbkrrqKNv2WzpTA/dYs0s3ApiTmKaJ1T/NkBdPJJgd2aPtAdc2JAY9VxYu7fW79bwQ8tr4Oe8jyngC2gLgxQ/0TC7r7VV6RQia+BY/qDnUD6+G6StitndXLANPBlJjR4408dhjn/Hee+UcOHDqx2v+/HxAsnXrSb9j41i27IyA/eT6mvHEbndRVdXO7Nm53HDDWZgyTD6NeXUjpeSYw8qmrjaqnHb0wGi9Eat0h9TYpeUvk916GFti4Po6KaXPsHnJ0Ru1kZ10QVe9tlu23yyAy+XA0llLUkoeOp0Bm5Q4kCQKwXhDAmMMJvSR/nCPFI05urSgky/9Nvh14kRjMY2SCJZ5IdJsDJE4SIM5zx96aCmrVl3S7diyHkEqkWRciYQPOlt5oLmKdR3N7LJ2MjqMH267xcwxp43tllPz5b1lMuk1S0GQrATN9bvIO7Ge5vpdIdtiTcxGCh3C7Qo4bhI6bFLS5j3uH9LsxTO1ESxDRMC1Q5wbsfM7zLWjznjismv5KxMye74mhCY2f7zTKodXw3u3aI9xyEBqbOrULH75ywv43vcCfyuWLZvCsmVndDt2BkKIfmc8aWmxcPKkmWuvLWblyrMZNSrJp7EPOlsBaHY5+Ed7A6vb6ml2OcnVG8nxGJVwGjthSEXvsve4phCCcd3cC96pS7rqtaTEXYHvx9JZS2pbOZbOWoQQJOp0pHmMarnDwg5rBy2uCHN+jhiN2SAxxExZHGksptOV3TMvdA8S6W8ARTDn+Q9/uI7u0yf+QSuRZoOIlAtTtB/NbL2BI3YLhjDvZ05SGlg8jx56y7LQqxM5iBN79JhSaj2PoZBCR0faeBKsLThM6QFTBnoEJx02RukNp3p6/lSsQxYuDWi797sMuHaIcyNe4Bru/O7vuzentt0MGVOCX9fby/Rn40pNhNNXaM+9j3HGQGssNdXEkSPNAcfeeusIBkNgnd7MKH3JeCKlpK3NRmurlZycFO6552wmTz412vZq7PzkDLZbzLzf2YJeCMZ6DZEf4TSWNOoMqP0k6PVPOgON30mnXTN0yZ73kRz4fpJS8ujwPPpjFDqMQoddutln7SDXYGKSKRGj0IWelhsxGuuA0SFy+caRxgZsnVz3LCZeA5WZmch99y2OuqHBnOfeIBM4NUXZvcxARH+5peSplhpcyOgc0L3gP1XpnaLs/rxP76dyI7isTHQ7yK/eSFdyHrSVgzBARqG2lEK6mZOYStLx9wKzL3imNvy3EQkaDOM/DdKXTA7RnB+J87z9BEz7Oky+pOd1/P0D3f0F3t5mDBmodXIDrbH771/Mt7/9Gm+88TkApaW5fPWr00NmRomk/oaGLjo77RQUZHDFFdMpKckLunWOxe3idXMTx+wWsg1GzWhESYKlkblbf6lN0XvO95+q9E5Rdn/eJ421VSClk670SRiFYKYpmdQTG3oGdIS5z4efxo7DvJ9A1sye14kjjQ1I4EnwIJFT4ulLbzOY8/yhh5ayZUsVcGqKMljQSqypctpocTt9kVixwpslwd+g9TtLgZTgskLNFjpSchFup2bgzFWQNh68jnYJ9U4HE7tlY5CFS6lz2qlFxwnPiNI/PZDvuxSif5kcIj0/Gud5sNx6QmhTmP5i806rJGQOm6CTwdLYmjUrWLDgaTo67Hz5y5OprGxnxoxsOjsd6PXh63e7JR0ddtrbbbjdEiklxcW5fOUrU5k2LStk+7rcLl5ub6DeaSevr0YHsCVl0zpqOqkdlT6/nBACvRABBs07dakXPdeVRYSUIJ0IcxUpgC19EnsbD1HUuJ+07FmB92SQ+3xYasxpgYQMGDU9+HXiSGMDNpILlcWkv6OrYM5zoMexgVyo+o65iQP2LrL04ZcfBAsyiWR7j3ABApHWGXAMAcfXYaz6F/PqPsWiT0KmT4CMKbiAFpeTdL0et4QFSWnaSqJu1w+VUaHH59zbFEdvRHJ+OOe5ENquxpYGuOCh0Bti9redUTBQI7nB1piUWhaS6mozBw82cPBgIw0NXeh0+EYfcKrjDzBhQiZFRTlMmTKagoL0XnPAdrhdvOTxveWECOjyp7d7f0zrEYr2PEpH6oQe93QwjbmkpMXlZJTeEBBIEux4wDE41XkE7PoEnCljKco+k0Sdrqfmu91vw05jbRUw4zoo/HL/rhMjBn0k57koDz64NECAsZg+jDRAZSA57rSRInqfpvQ6wAGmJySHPNadcO8x0jp7HJu4FEfNFhoSx5BlbcDq8Ve1OB1UOW2MJ4FEnRaE0n1bIO+I0l+AIadOux+L9ruI5HxvD9RfgP5TLV11MOGi8Ds+97edccBga0wIKCjIoKAgg7PPHg9AR4edxsYuHA4XDocbnU5gNOpISDAwZkxKxHlfQRvBvdhWR5vLFZGBg97vfVPmNJqyS8hsOYQl+ZQ/LdR7bHE5qXLaAMj2a0Ow4z2OZUzxGTmTywZpE9hr6yRVp2enZzdun+aDXH/YaMzaDCljoeD8/l9nEBiwHFShsphEM3KMR6xuN20uJwkRfGGTTImcnZjmcyZLKQOO9eWz6F5nRMekhOPad1Gbmo9euqD1KEjJKL2B8YYELegEsEhXj3aFCoYZsu8ylPNcSu3P7YLx8Z84tr/Eg8ZSU00UFmYydWoWM2fmcOaZ2UyZMprx49OjMnAAH3W10eJyBhiX3uhVY8AXU65EosNoN4evDHroIdzxgGNSaiM5P0ztX2BAYHW7WZCUHqDPYasxhwVs7VB8fehsQnHGgBi57g5st/se7rhjAatWbRn2hq7J5UAQ2WjRJHRMT0jGJHTssnbwmdWMEcH0hGSMCD6zmtnl6eFFin+dER3zTFVSswXGLsB83v/RNWoaxrZj0FaOHq0XqhdawqM91s4AcUkpebOjyecf+E5GLjMTkjlg6xoaEXZ3np99j/ZYu0U7bmmC0VMhLb/3uoYxI01jFXYrOy1msntxAXQnEo1tIYEDRTdjsrehd1rC1qcXwqeH3o77jkGgjrQySgAAIABJREFUn3v8+dqjuYqE9i9w4CZJ6Hz69LZzWGrsizeg6HoYNXVw29QPBmS6MtIsJsORRqcDd+/FAhjITCa9IgToE2HsApioTTdUzbqJadt/j0MYAqYQDECb28UxT4okbzubXU5G6w0Duy1ONO8nnPPc0QGTLh3cNg0BI0ljNrebtzuaSNdHmT3Ej9401pYxhUMzvsOM/c/QlZKPO0pjGhYhtEjltPGnlq1keBJaCwPJQk+l00a2wUiKZ7lB961x4l5jE78M9lbIKYHx5w1ue/rJgG61MxCJhoeat81NHLJ3MToSkfhlJ5F6U+hMJn3J5uFHsMwqPTKjSOnLaKDPOIO523+Ly5CEU+i1vaEyJuPWm7BJNzoEB+3hHeDhvstIgmv6TTCntt2sLVDNLtKc4n3JpmBrj+uMJ90ZCRrbZengnY5mxhr7MP0VpcbGWluYUvEGtoRMbb1ohNilmxqHnbFG06l72u0MzI4iJfhnTBF67R512rCYK0lPL2BWSlbEQSZxozGHBTprYOISmPkf2vq4/mhkkDU2oPvCRLwKfxhhdrsiX7PTbYuNkJlMos000I1gWR961Ol3HVfbUY5O+waJ1gZoLdcCNdq+QCcELmBeUpiMK3irC/1dBsvaEHN6XF+CtVHbsfj97/c9m0KcZzzpzkjQ2D5bJ6l93S8uSo3VJI5ib8n/Q0g3SZ0nT4WB9kKNw06L20mNw28ReffsKEIEHvN+F21fkNhRTau5CqfHcIXNaoS3ujjQWGc92Jqh9Psw81vaesP+amQ4Zzw5HXBIGXnPwC+DQNhMJsG24oiCYFkfgm7v4XesSW+iIaeMrPoddIlcyJjsK7bVEsW2HUHoLavLgGCugoILYepVkDqu79kU4jzjyUij0+2i2mkjp6/Th33QWJshgZ1z/pszPl9NVtNeupLH4u4liGKs0QQOAkebwbKjBDuWMVnbwidlHO1uJ6N0hui2xgnCgGvM7QJzpbajeun3wD/TS381MlIyngwnopny+UtrLV1uV1SZTsJmMjEmMS/J7+YewLUk3THa2piz7Tc4Dck4jdqi0+MObTNV7/SJ/6LUPmdcGWhs7YAbFv1ay1cZZwzkdOVwIZTGDtm6WGNu7HdihT5pTLoZW/0vJn+xBpfOhDUpJ/ba89Ozxe1itN5Am9sVoKm40piUYG3R/G+Fl8C0rw2LKMohWSc3XPhLay0dbhe3jRqHd0HrYy0nSdXp+c/MvB7l9Z6NFqMhVCaT/NpPMbnsiCmXaQW9of76Pu6fFg2VG3G4rByZuoIZB5+hQz8e0X6MFGEiL218fDjAI0G6tWnKeT+NSwOnCK+xqaakmPhM+qqxmvGLacucSuGxNxnVvB+nMRVbwujYGLu2CpBOXzCKAUFnyxEmWOoh7+z405itTVsD5x29ZYXISznMOK2NnJSSDreLV8zatiK3jRrHYy0necXcyNfSsoOO6IwieiMHUJqYGlCfAMYhEQ07wGDUIh/9Qv0HdETnl+qrKW8+9Tlzyal4k067haTMKcxPTPVbACyGvncZCim1/HkTlkD2rKFujSIIvWnM4nZFvkVNL/RVY12p+RwouoW09mMUHnuT9LZyXPpErEnZ0NegDk+qL+/icDKmoGs/hquzjjyd9O39BkOsMSk1w2Zv1xZ4l/0QxpT0/X3HIae1kRNCcNuocQC8Ym70CfFradm+Xmd3knQ6nM7IzFz3CKiA+oRAFC7VlFizRfuDgFD/SOqMCv+Iy4mesPuaLRx1uzC5nKSZUuhKzR8ewQxeA5c7B8785lC3RhGC3jS2sas10tiPoMRMY0JgzpjM9tk/oK35c86p/YQxzfuQ6LAnjMJpSIqs0+kfceldRmCuAnMVUugRqfmQWzz0GnNawdKotXfUVJh9s9ZRHEHGzcvIe0dR4i9CL6EMHMB4QwI2GdlKud72jtPWn3Tb2ymMgYuoznD4R1z6Xdut03NwdBEtufMZY6kjDk1aIFKC+YQ2nTL7FojlmidFzAmnsSShx9WPncZjrbFjDhtvG0fz1tTr2DH3Z5yYuAwpIKWjmpSOKgyOjvARmd2jK72GDpBChz5j0tClkHNYtGCS9hPg6NTWvp1zLyz4GeQUj0gDB6f5SA7w+Qf8eazlZEhDF2lOPYggAsov3ZaP4+vCirBfUVX+EZfdru3SGdk8ejbLjUmaCNInDJ0Yw+EdweUUQ8n3+7SuUDG4hNNYnsHUDxMXe43512cRyVRNXErVhItJ7qplVPMBcms3k9JZjQQcpnSchhSkfxCaf3Rlt1Rfdp2JnKb9kL9gcLQl3Zoxs7YCbjBlaIkSxpwFGYUj1qh157Q2cl7xef0D/v4CCD6i8+48EMmiW2/KoRAXD/QP+PsLIKQIw9bZG4YEbXovxLVtrcdw6dEMSP1OSJsQX6Mklw3M1TD2bJh947CI+jrd6U1jN2fmYRBa9v+++OZirbGg9QlBV8pYulLGUj3+QpIs9WQ2HySncRep5kqEdCORIPQ4jKk4UnIBXWCqr4wpSHMlo0+sA0dr5JucRop0g6PLkxDB7snCIiA1H/LPg9yzIK3gtDFs/pzWRk4IQapOH+CD806rpOr0QY1YstCRrNPjQGp5Ift+8R7ptnzTKvoI9obqDyGuLfSfMLrzKJz1/7N35vFRVXf/f5+ZyWSyLwQIBJKwCwQSdlFwKzVqVXBD1LZuVdvqT8vvsa1t/bV2t7UtYp+2PlRr+/RR0acqtXWhIq5FdhBZRLaQAAnZt8ns9/z+uJlhJplJZpKZJDOc9+sFk7lz7r3n3rmf+Z5zvt/zPf9Hz1136CUwpeoTrAcTKfXhH82pT0gtvASiuFitInb0pjFzZ4TlUaeNnGg3qGKhMSGwpY7EljqS6jEXITQPFns9qR01pLceJ7vlEGntJ/Vn1tGMMFmQphRctnpSzBmk50zUG2eRnltqeiPP4zzzT3P5GS2pG7QRZbqPLW2UPrdNNQTVPDmIPDXS39vqOeq0B2QkDzcgpFs5KXEiA9d+i1QAXVN4hYtfBKdbSpo9blYOG3OmRd1aBZ88pQ8PphcMTq/O44D2U7pwS+6E9FGxPV+cpfWKF3rS2GGnjRdbaxllDL1AaiQBV4OtMaG5SbHVYbE3YHK2k+xoxtlRy3iPlSmaTQ/V15x+Bsq/LtLvfedvs5R62eRsSMkFyzBIGQ6WXP0ZteTqRi1ehu4HWGNndU/OS6SRTtOT0zjgl3cOwlsnLmg5ITjmsIW1b0i8ASWgD0eGi991NnvczLCkBQ4ZZY6Fcx8+06szmiFlxMD0ojSPnm5MemDqFweu9+ZNOQR6xJkiKvSksfFJFsYlpXDK5WBYCJ93uPoKWnaANSYNJt/wJujLc9mkh8U5o/VnWEq98eZs16cZSO3MPyRg0MsJg57/MikNwo3ujAcGWGOJYeQGcAVagOIkCynCgEPTSO5cYDTcgJBg5fqdoqefacGklLiRlFnSu39oTIIJV+rDIEdehZptgNBbkiZL9O+7y6ovlyOE7nsbf2Xse2/+qLRewYmhxgxCcFl6Dk811eCUWtCeWiQaGUoak1LSqLlZlj6MdG8jTQhdO6Yw6jLAv20DgkrrFSGbHgFHM1y0qjNVj4R3V+pd+/MeidlpP+po4f2OFkb2Mx3RUKBNc5NpMPHlrJG9z9exN8Gpj+CjH+oTSMdeApYcvcVZsV4XbqTZWjwucDTpjnNLDoy7XPejJGf1+ZqGEnE/XDlAGttua+Nf7Y3km8x9XnJnqFHndlGUlMwNmcMjnws3SL9t8cigrUIQczqdu+xcrX/53odg52p9ewwXG5yerKeQ0uJsccpgWDWNBeFmXPA3Qo2fQv0n0FEHn/1NX1TRVge2Rt1wSe3MdyBlp/PcqWdYaK0888/eCLnnwLwH4cJfQfGlCWPg4p4B1NgsSzozLWmc9rgSQlf1bhfZRiOXp+dGbuAG8bct0Yjv4Uoh9FYO6F/+ztX637MfONP6CYJV87DR2swladln1l+LkEyjiSnmFI647AwzJgV1jAd1lvc1SCQKOF02Gmt3kzuiDHNSCgAOqWERBsYnp4R/ICHg4tW6z2Dnajj5gb79nJth8nJoOqgHq3gcekYFbyvUYNJ7eplFkDtVf00bBSnD4n8IJlEZQI0ZheDy9GEYhYFdtraAHl2owJOhqjGZN4N8cxo3ZI04M0wZCX2874ruxLeRgzMPg/chgF4fgo3WZn7TqOeUuypjWJ9PvTg1m8+aq3F1Cq2rYzuos7yvQSJRoLF2N/mV/6IGyC9YiJSSBo+LL6QPizxFWLD7fsX/dL/vUtODSLyOdEX8MYAaMwpBeVoOScBWexvDjWaShAgZeDLUNNZQuxtD3R6ElNw4bWlEq5V0ow/3XdGd+DdyUiLf+UZgEO4730Bc/HjIh+GStOyA174yzJTExWk5bLA2Upyktxh7dXb3M0ikP+SOKKOm8xWgweNmQlIKM5L7kL3fO3ziz7sru4tQGKCvC2IqhgYDrDGDEFySlkO60cR71mYswhAycGQoacyuabiGlZCrOblp/IX9M3AQvsYUPRLfvz5Ssn/93YhdTyBn3Q//V0POuh+x6wn2r7875Lh1msHIVRnD+jxU6c9sSzpjTBY6pMaU5NSAHpE3e0JAL8mbdWQQ5rSYk1LIL1iIOSnFl3/zsvTcyJ38/v6B2Q/A/9X0V3//gSIxGCSNGYTg3JRMbsvOJ91gpMHjDjpHbihoTJOSWrcTm/RwffZo7i1ZzrDUvo8QAUpjUSSue3ISaE/K4G8Tb6am9JvcC/yu9Jvkt9VTmJQRMK0yVhiF4Ir0XJ7uHLZMioMhOf9hyixjHx4BIfQIL3//gNd/kJytWpkJxGBrbKTJzG3Z+ey2t/NuRzNu6SbLYMJiGHyduaWkyePCgx40syg1KyoNZ0BpLIrE/RQCKSW/azzJS+0Nvm3XpQ/j3tzuS8bEko3tTfyxuZrzUjLJ6IvhGCCklNR6XIw3W7guY3j/QrUTcQ5PDIj3KQRDQWN1bie/bzzFeamZfOq00a55SBMGMkKk34sldk2jWXNjRFBqSWOWJSOixO0RoTQWFgmd8UQIwb25BQECHGgDB7Cpo4Uaj4tNtlY+n5YzZOf51GtuRpnMXJ2e1/86dt1/iF6zon8MBY2taarmXVsLJiH4dl4hx1w2Pupo5ZTbCeg5ZdMMxqgtwOqPlJIOqWHVPGhAhsHI59NymJacSkqss/AojfWbuDdykS6VE6s63JM7Ghr1CK/PnDbyTeaYCK6vSCmp19wMM5i4PnO4L1OLQtEbg60xKSV35+hZb+7OGYURmGROZWJSCvUeN1UuO585bVS5Hb75dSnCgFkYMAsRUWNOSokLiVNK7JrmW+tuhCmJMks6Y5OSKTAlDyltK3omro1cX5bKiTZ/bq6hXfNwb85oHh5RjEfT+G5dBUc7WliYmoVpCIjBO0Q52pTM9Zl5sW99KhKGwdaYv76+N7zIV590g5HbsvMZbkpiuCmJ2SkZuKTGabeLSpedk24HTR439R6Xz2/o75jp6kv0/zzdYCTXYGJkspmiJAujTeb+R0oqBo24NnJ9WSonmkgpadc8AYL/Q3M1W+1tnGfJoM7jIk0YyBxEH51D02jQXEwxp3Jl+jDVg1NExGBqLJi+/A1u15UNkoSBMUnJjEk6E1WpdQ41tmse2jUPbinRkGgSDAIEAiN6NGh65z/VS0ssBi3wpLXVwdq1e1mxooTMzP6F+va0jEc0spv0dm7/li3g+0GocTv5Z3sDDW43w01JAyoePYLSjRBwaVoO05PTsEktpvdCEZzBCDyJpr4gtMYGU18D7XcPh1jfD0VwhmTuyrVr93LPPf9k7dq9/T5WT8t4eDMvbLQ29/s8oc7tbdl68QpwVFIyt2Xnc15qJrUeFy0eN5E0KvqKXdOo9jgpMifzlexRzLCkYxAi5vdCMXSIpr4gtMYGU19DEaWxocegjaOtWFES8BoropXdJBS9OeWThIEL0rKZZE7hTWsTp91OkhBkG01R7dlJKWnVPHRIjVRh4Kr0YUxPTgtwusf6XiiGDmeLvoYaSmNDj7ifJzeY9OSUDzakIqWk2u1kt72dvY4OJJJMgxGLMIQl2GBDRm4kzR4PHiRjk5JZkJJJcZJlSAS8KHTifZ7cYBGpvqJxvlBuD8XQJqHnyQ0mkTrlhRCMTkpmdFIyF6Zl86mjg+32Nk57XL5IL7MwkCoMJAmB6NxHSsluezsOqTHVnIq9Mw7sgKMDizBwQ9ZwZianx25CqkIxCAxk0It/FKdXc/5RnIr4RRm5fnJbdn5Ai88rxN4EmGYwMiclgzkpGdg0Dw0eN3VuJyfcDk64nDR63LiRCEADWjQPx90OzMLATVkj2Ght5rjbwbXpw7gkNVu1OBUJSV/1FQmRRnEq4gtl5KJAT4Ev4ZBiMDLGYGRMUjKzyPBtl1LiQY8OEuAT3id1FUDPUWYqykuRKPRXX+Ec39tDfKmt3mfsehsSVRqLD9SkqSGMEAJTZ8aGSKPMVJSXQhE+fYniVBqLD1RPLk6INMpMRXkpFOHTlyhOpbH4QPXk4oCuUWYbC2dyXUYeL7XV87umU0Hn3kVzzTyFIpHpi75AaSxeUD25OGCw05cpFImM0ldio4xcnDAQUWYKxdmK0lfiooYr44hYR5kpFGczSl+JSUIbua5j6QORN1KhOFtQ+lLEAwlr5P7cXBPgNPY6l//cXDPINVMo4h+lL0W8kJBGzj+DgVeI3uipds2jWpwKRT9Q+lLEEwkZeNLXDAYKhaJ3lL4U8URC9uQg/tahUijiCaUvRbyQsEYuVAYDNZSiUPQfpS9FvJCQRq6vGQwUCkXvKH0p4omE9cmpDAYKRWxQ+lLEEwlp5EBlMFAoYonSlyJeSMjhSi8qg4FCETuUvhTxQEIbOYVCoVCc3Sgjp1AoFIqERRk5hUKhUCQsysgpFAqFImFRRk6hUCgUCYsycgqFQqFIWJSRUygUCkXCooycQqFQKBIWZeQUCoVCkbAoI6dQKBSKhEUZOYVCoVAkLMrIKRQKhSJhUUZOoVAoFAmLMnIKhUKhSFiUkVMoFApFwqKMnEKhUCgSFmXkFAqFQpGwKCOnUCgUioRFGTmFQqFQJCzKyCkUCoUiYVFGTqFQKBQJizJyCoVCoUhYlJFTKBQKRcKijJxCoVAoEhZl5BQKhUKRsCgjp1AoFIqERRk5hUKhUCQsQkoZfmEh6oDjsauOQpGQFEkph4dTUGlMoegTITUWkZFTKBQKhSKeUMOVCoVCoUhYlJFTKBQKRcKijJxCoVAoEhZl5BQKhUKRsCgjp1AoFIqERRk5hUKhUCQsysgpFAqFImFRRk6hUCgUCYsycgqFQqFIWJSRUygUCkXCooycQqFQKBIWZeTiCCFEoRCiXQhh7MO+xUIIKYQwxaJuCkW8I4SYIoTYJYRoE0Lc30tZKYSY2Pn3n4UQPxmYWioiRRm5IY4QokIIsQRASlkppUyXUnoGu14KRQLyLeBdKWWGlPKJwa6MIjooI6dQKBQ6RcC+wa6EIrooIzeEEUL8FSgE/tE5TPkt/yFHIUSWEOJpIUS1EOKkEOIn3qFMIYRRCPErIUS9EOIo8IVBvBSFYkgjhNgIXAz8Z6fWTgkhvuL3+W1CiA8Hr4aKvqKM3BBGSvkloBK4SkqZDrzYpchfADcwEZgFXAp4hXkXcGXn9rnA9QNRZ4UiHpFSXgJ8ANzXqbXPBrlKiiihjFycIoQYCVwOfENKaZVS1gKrgBWdRZYDj0spq6SUjcDPB6mqCoVCMWioSLv4pQhIAqqFEN5tBqCq8+/Rfn8DHB+4qikUCsXQQBm5oY8Msb0KcAB5Ukp3kM+rgbF+7wujXTGFIoGxAql+7/MHqyKK/qGGK4c+p4HxXTdKKauBfwG/FkJkCiEMQogJQogLO4u8CNwvhBgjhMgBHhq4KisUcc9u4FohRGrnfLg7B7tCir6hjNzQ5+fAw0KIZroHj3wZMAP7gSbgb8Cozs/+CKwHPgZ2Ai8PSG0VisRgFeBEb2T+BXh2cKuj6CtCylCjYQqFQqFQxDeqJ6dQKBSKhEUZOYVCoVAkLMrIKRQKhSJhUUZOoVAoFAlLRPPk8vLyZHFxcYyqolAkJjt27KiXUg4Pp6zSmEIROT1pLCIjV1xczPbt26NTK4XiLEEIEXa2GaUxhSJyetKYGq5UKBQKRcKijJxCoVAoEhZl5BQKhUKRsCgjp1AoFIqERRk5hUKhUCQsysgpFAqFImFRRk6hUCgUCYsycgqFQqFIWJSRUygUCkXCooycQqFQKBKWmBi5hoYGysrKKCsrIz8/n4KCAt97p9MZ1jFuv/12Dh48GIvqxQXPPfccM2bMYPr06XznO9/xbX/qqacYPny4734+88wzQfdftGgRU6ZM8ZVraGgYqKorBgClsf4TSmNe1q5dixCC3bt3B92/sbGRa6+9lnPOOYepU6eydevWWFdZ0ReklGH/mzNnjoyUH/zgB/Kxxx7rtl3TNOnxeCI+3lDA7XbH9PinT5+WhYWFsr6+XmqaJm+++Wb57rvvSiml/OMf/ygfeOCBXo9x/vnny127dsW0norwALZLpbGIGEyNSSllS0uLXLx4sZw7d25IHd18883ymWeekVJK6XA4ZHNzc0zrrAhNTxob0OHKw4cPU1JSwle/+lVmz55NdXU1d999N3PnzmX69On86Ec/8pVdtGgRu3fvxu12k52dzUMPPURpaSkLFy6ktra227E3b97MwoULmTVrFueffz6HDh0CwO12s3LlSkpKSpg5cya///3vAdiyZQsLFy6ktLSUBQsW0NHRwVNPPcU3vvEN3zEvu+wyPvzwQ18dHn74YebPn8/WrVv5wQ9+wLx583zXo99n+Oyzz7jkkksoLS1l9uzZVFRUcNNNN/Haa6/5jnvjjTfy+uuvh7xPR44cYerUqQwbNgwhBEuWLOGll17q381XnBUojen0V2Pf/e53+e53v0tycnLQ/RsbG9myZQu33XYbAGazmaysrN6+HsVgEMr6BfvX31bmoUOHpBBCbt261fd5Q0ODlFJKl8slFy1aJPft2yelPNMTcblcEpCvv/66lFLKlStXyp///OfdztPc3Oxr/b3xxhty+fLlUkopn3jiCbl8+XLfZw0NDdJms8ni4mK5Y8eOgH279pLKy8vlBx984KvDSy+91K3emqbJFStW+Oo3e/Zs+eqrr0oppbTZbNJqtcoNGzbI6667TkopZWNjoxw3bpx0u92ysrJSXnXVVd2upa6uThYUFMjjx49Lp9Mply5dKpctWyal1Htyo0aNkjNmzJA33HCDPHHiRND7fv7558uSkhJZWloqf/rTnwYtoxgYGMCenNJY/zW2detWecMNNwTco65s27ZNnnvuufJLX/qSLCsrk3fddZe0Wq3BvyBFzOlJYwMeeDJhwgTmzZvne//8888ze/ZsZs+ezYEDB9i/f3+3fVJSUrj88ssBmDNnDhUVFd3KNDc3c+2111JSUsKDDz7Ivn37ANiwYQNf/epXMRqNAOTm5nLgwAEKCwuZPXs2AFlZWb7PQ2E2m7nmmmt8799++23mz59PaWkp7733Hvv27aOpqYn6+nquuuoqACwWC6mpqVxyySXs37+fhoYGnn32WZYvX47RaGTs2LG8+uqr3c6Vl5fH7373O66//nouvPBCxo0bh8mkr4q0bNkyjh07xp49e7jwwgu5/fbbg9b3hRde4JNPPuH999/n7bff5rnnnuvx+hSJg9JY3zWmaRr/8R//wa9+9ase6+p2u9m+fTv3338/O3fuxGw289hjj/W4j2JwGHAjl5aW5vv70KFDrF69mo0bN7Jnzx4uu+wy7HZ7t33MZrPvb6PRiNvt7lbme9/7HuXl5ezdu5d169b5jiOlRAgRUDbYNsD3kHvxr0tKSopvn46ODu677z5eeeUV9uzZwx133OErG+y4QghuueUWnnvuOZ555pmQhsmfpUuXsnXrVjZt2sSkSZOYNGkSoIvTO4Ry9913s23btqD7FxQUAJCZmclNN92knOJnEUpjfddYc3Mz+/fv54ILLvCt7XfFFVewa9eugH3HjBlDUVERc+fORQjBddddx86dO3s9p2LgGdQpBK2trWRkZJCZmUl1dTXr16/v87FaWlp8P+x//vOffdsvvfRS/vCHP+DxeAB9LH369OkcP37c91C2trbi8XgoLi5m165dSCmpqKhgx44dQc9ls9kwGAzk5eXR1tbmG8vPyckhLy+Pf/zjH4Au4I6ODkCPZHvsscewWCxMmTKl1+vx+kQaGxt58sknufPOOwGorq72lVm3bh3Tp0/vtq/L5aK+vt7392uvvUZJSUmv51QkHkpjoQmmsdzcXOrr66moqKCiooK5c+fy+uuvM2vWrIB9x4wZw4gRIzh8+DCg9zqnTZsW1n1UDCyDauRmz57NtGnTKCkp4a677uL888/v87G+/e1v881vfrPbMe655x7y8/OZOXMmpaWlvPjiiyQnJ/P888/zta99jdLSUi699FIcDgcXXnghBQUFzJgxg4ceeoiysrKg5xo2bBi33norJSUlXHPNNSxYsMD32bPPPsuvf/1rZs6cyaJFi6irqwNg9OjRTJ48OaCFWVVVxdVXXx30HPfeey/Tpk1j0aJFPPzww0yYMAGA3/zmN5SUlFBaWsof/vAHnn76aQA8Hg9z584FdOGXl5f7rrm4uJg77rijj3dWEc8ojUWusVB0PdZvf/tbbrzxRmbOnMm+fft46KGHer6BikFByM6IpXCYO3eu3L59ewyrk7hYrVZmzJjBxx9/TEZGxmBXRzGACCF2SCnnhlNWaazvKI2dvfSkMZXxZABYv349U6dOZeXKlUp8CkUMUBpThMI02BU4GygvL6eysnKwq6FQJCxKY4pQqJ6cQqFQKBIWZeQUCoVCkbAoI6dQKBSKhEUZOYVCoVAkLMpMC188AAAgAElEQVTIKRQKhSJhUUZOoVAoFAmLMnIKhUKhSFiUkRtgWlsdrFmzg9ZWx2BXRaFISJTGFP7ExMg1NDRQVlZGWVkZ+fn5FBQU+N47nc6wj/OnP/2JmpqaWFRx0Fi7di/33PNP1q7d69umaRrl5eVkZ2ezbNmygPJHjhxh/vz5TJw4kZtvvhmXywXo+Smvv/56Jk6cyMKFC0NOhH399deZMmUKEydOVEuBJBBKY6EJpjGABx98kJKSEkpKSvjb3/7m237eeef57t2oUaO4/vrrQx67paWFUaNGBSz8qhjihFpoLti//i7oGCmhFiwcSFwuV1SP19Jil//1X9tlS4vdt03TNLlhwwb5yiuvyKVLlwaUv+aaa+T//u//SimlvPPOO+WaNWuklFKuXr1a3nvvvVJKKf/617/Km2++udu5nE6nHDdunKyoqJB2u12WlJTIgwcPRvV6FL3DAC6aGilni8bWrVsny8vLpdvtlm1tbXL27Nmyra2t275XX321fPbZZ0Me++tf/7q86aabAhZ+VQw+PWlswIcr//KXvzB//nzKysr4+te/jqZpuN1uvvSlLzFjxgxKSkp44okneOGFF9i9ezc33nhj0Nbpk08+ybx58ygtLeWGG27AZrMBUFNTw9KlS30Z0bds2QLAM88849vmzVL+xS9+kXXr1vmOmZ6eDuiLQC5ZsoQVK1b4lti46qqrmDNnDtOnT+epp57y7fPaa68xe/ZsX6Z1j8fDxIkTaWxsBPTVAcaPH+97n5mZzN13zyEzM9l3DCEEn/vc53zn9+LxeHj//fd9C0neeuutvvr+/e9/59ZbbwVg+fLlQZdQ2bx5M1OnTqWoqIjk5GSWL1/O3//+9/C/LEVcojTWXWP79+/noosuwmg0kp6eTklJCf/6178Crre5uZkPPviApUuXBr2vW7dupbm5mUsuuSTCb0QxqISyfsH+9beV+cknn8ilS5f6Wm533XWXfPbZZ+XmzZvlZZdd5tunqalJStlzK7O+vt7397e//W35+9//Xkop5bXXXit/+9vfSin1FmJLS4vcvXu3nDJlimxoaJBSSt/rLbfcIl955RXfcdLS0qSUUr711lsyLS1NHj9+3PeZdx+r1SqnTp0qGxsbZXV1tRw7dqysqKgIKPPwww/76vDaa6/J5cuXSymlfPnll+UPf/jDkPfqrbfeCujJVVdXyylTpvjeHz16VJaWlkoppZwyZYqsrq72fVZYWOi7b16ef/55ec899/je/+lPf1It0EGAAezJKY0F19hrr70mFy9eLDs6OmRtba0sKiqSjz/+eECZp59+Wt54441B74Xb7ZYXXHCBPHnypPzjH/+odDTE6EljA5qgecOGDWzbts237pnNZmPs2LGUl5dz8OBBHnjgAa644gouvfTSXo+1Z88evv/979Pc3ExbWxtXXnklAO+++y5r164F9FWIMzMz2bhxIzfeeCO5ubkAvteeWLhwIYWFhb73q1at4tVXXwXgxIkTHDlyhKqqKi6++GKKiooCjnvnnXdyww03cN999/GnP/2Jr3zlKwBcc801vl5ZOMggyyB5V0Xu6bNw9lckJkpjwTV2xRVXsH37dhYuXMiIESNYuHAhJlPgz9/zzz/PfffdF7Suv/3tb1m6dCmjR4/u9boUQ4sBNXJSSu644w5+/OMfd/tsz549vPHGGzzxxBO89NJLrFmzpsdjffnLX+aNN96gpKSEp556is2bN/s+C/ZjH+zH3WQyoWkaoA95uN1u32dpaWm+vzds2MD777/P5s2bSUlJYdGiRdjt9pDHLS4uJicnh3feeYddu3aF9YMSjBEjRlBfX4/H48FoNHLixAmfyMaMGUNVVRX5+fk4nU6sVitZWVkB+3vLePHfX5GYKI2F5vvf/z7f//73AX2If9KkSb7Pamtr2bVrF5dffnnQfTdv3symTZt44oknaG9vx+l0kpaWxk9/+tNez6sYXAbUJ7dkyRJefPFF6uvrAT1CrLKykrq6OqSU3HDDDfzwhz9k586dAGRkZNDW1hb0WFarlfz8fFwuF88995xv+8UXX8yTTz4J6KJqbW1lyZIlrF271jdm730tLi5mx44dALzyyit4PJ6g52ppaSE3N5eUlBT27dvHtm3bADj//PPZuHEjx48fDzgu6C3NW265hRUrVmAw9O02G41GFi9ezCuvvALovhavv+Dqq6/mL3/5CwAvvvhiUJGfe+657N+/n+PHj+NwOHjxxRdDrpKsSAyUxoLjdrt9++7atYsDBw7wuc99zvf5iy++yNKlSzGbzUH3X7t2LZWVlVRUVPDoo49yxx13KAMXL4Qaxwz2LxqRX88++6wsLS2VM2bMkLNnz5Zbt26VO3bskGVlZbK0tFSWlZXJ9evXSymlfOGFF+TkyZNlaWmpdDgcAcf97W9/K8eNGycvvPBCee+998o777xTSqn7sa688kpZUlIiy8rK5JYtW6SU+nj79OnTZWlpqbzjjjuklFKeOnVKzps3T86bN09+73vfC/AX+PvGbDabvPTSS+XMmTPl8uXL5eLFi+UHH3wgpZTyn//8pywtLZUzZ84M8Hk4HA6ZmpoqP/vsM9+2nnxy5557rszLy5MWi0UWFBTIDRs2SCmlPHTokJw7d66cMGGCvPHGG333oaOjQ1577bVywoQJcsGCBfLYsWNSSikrKyvlVVdd5Tvuq6++KidNmiTHjx8vH3300bC+M0V0YYCjK5XGumusvb1dTp06VU6dOlWee+658uOPPw74/Pzzz5dvvfVWwLbNmzcH+LS9KJ/c0KMnjQkZxG8Tirlz58rt27fHzOAmEps3b+Y73/kO77zzzmBXRTHICCF2SCnnhlNWaSx8lMYUXnrSmFoZPAb89Kc/Zc2aNT7nvEKhiC5KY4pwUT25KNHa6mDt2r2sWFESMD9HoVA9uf6j9KXoiZ40pnJXRolQqYQUCkX/UfpS9BU1XBklVqwoCXhVKBTRQ+lL0VeUkYsS3lRCCoUi+ih9KfqKGq5UKBQKRcKijFwnag0qhSK2KI0pBgNl5DpRjm2FIrYojSkGA+WT60Q5thWK2KI0phgMlJHrRDm2FYrYojSmGAyUkVMoFEMCl8tDfX0HVquLjg79X06OhaKibCwW9VOl6BvqyRkCqGwOirMVm83FZ581sHXrSbZvP4Xbrfk+0zR9mR0hYPLkYSxdeg7nnJPXp/MojZ29KCM3BPA65AE1nKNIeKSUVFQ088Ybh9mx4xSaJklONpGXl0pSkrFbeY9H48SJVn72sw+45ZYZXHrphIgX/1UaO3tRRi4CZJcFHLu+7yvKIa84G5BScuRIEy+/fID9++tITjYyenQGRqMhoExXjRmNBoYNSyUzM5lnn/2ErCwL5547JqJzK42dvSgjFyaPPPIuzc12Vq0qRwiBlJKVK9eTnW3hkUcu6texlUNekeicPNnK3/62n507q0lLM1NUlNWtgfjuuxXY7W7Kyyf4NLZ+/REsFhMXXVRMUpKR4cNTWbfuUxYsKIiogak0dvaijFwXpJQ4nR6f49tmc2O1OjlwoI4XX9xPTU073/rWeTz++Bb++tc9fPWrc3C7PZhM3YdZFIqzHZfLw5tvHubllz/FbDZQXJwd1DhJKbHb3WzZchKA8vIJrF9/hC1bTrJgQYGvh5eRkczx480cOdLExIm5A305ijjkrDZymiapq7Ny6lQbx4418+mn9Rw/3ozT6cFg8B8ygdTUJKZOzeOFF/bxwgv7AJg2bTgOh4evf/11pk8fzqxZo5gwIYdRozIC9lcozkYqK1v44x93UFnZwpgxmUH9bV6EEJSXTwBgy5aTPmO3YEGBr2fnJSnJwKZNVcrIKcLirDJyHo/G8eMtfPLJafbvr+PYsWZfNJcQkJGRzPDhaZhMwRPBFBZm8aMfve97f/31UxFC4HZrHDrUyO7dpxFCYrEkMW/eaK64YhIjR6YPyLUpFEMFl8vDG28c5pVXDpCamsS4cTlh7ec1dF4DB3QzcAApKUnU1VmjWmdF4pLwRs7l8nD4cCM7d1bz0UcnsFqdGAz6sMeIEaENWle8/gF/1q8/Qnn5BEwmA3l5qeR1Rjc7nR42barigw8qWbJkHFdcMZnsbEu0L02hGHLU1Vn5z//cyvHjLRQUZPTYe+tKTxrzN3RGowG73R21OisSm4Q0ci6XhwMH6tmyRZ9743S6/QxRasTH84rP6x/w9xdA99am2WykoCATt1vj7bePsXFjBcuWncMVV0xSw5iKhOXgwXqeeGILHo+kuDg7on0j0ZjBILDbPVGvvyIxSSgj19hoY9OmStavP0J7u7Nz7k1KRK3JYAghsFhMAf4Br//AYjGFjPIymQyMHZuF0+nhhRf20dLi4KabSpShUyQUUkreeecY//3fe8jNTenTZOtINCYEaJoW6lAKRQBxb+SklBw/3sL69YfZvPkEQgiGD09l2LDIe2w9cdFFxQFzeLwiDCeM2Ww2UlycxZtvHsJoFNx44/SozK9TKAYbp9PD889/wttvH6OgIIPk5L7/pISrMavVxaRJw/pVb8XZQ9wutSOlZP/+On7xi3/zyCPvsnNnNWPGZFJYmEVKSlKP+9bVWfnjH3dG7LzuKrZIDJXRqIdPv/LKp3z966+pNbWGKo5W2LNGf41GuQSmrc3Br361iXfeqaC4ODvAwMVSYzabi0mTQkdWqnXrhjgDrLG4NHJVVS089tgmfvGLDzlxopWioixGjQrMnNAT69Yd5NSpNtatOxjjmgZiNBpoa7Pz5JM7WLNmx4CeWxEmB9fCW/for9Eol6C0tNj55S83cfRoE0VFWd2G4GOpMSEEo0dnhPxcrVs3xBlgjcXVcGVzs51XX/2UjRsrSEkxhZxY2hvLlk1h3bqDLFs2JaL9opHWq7Q0n9ZWZ6+9TcUgMWVF4Gt/yyUgTU02fvnLTdTXWxkzJjNomVhpTEqJlJCfH3pqjkrhNcQZYI0JKWXYhefOnSu3b9/erxP2BYfDzTvvVPDSSwfQNK1bvruBoLeUQ5HgdmucPt3O6tWXk5qqjF2iI4TYIaWcG07ZwdJYuNTXd/DLX/6blhY7o0aF7k31hXA01t7uxGgUPProEuXXVvjoSWNDfrjyk09O853vvM3atXvJy0th7NisATdw/imH1q8/EhDubLe7iaShAHrUpdstOXSoIUY1Viiiz+nT7fzsZx/Q1uaIuoELV2ONjTYuuKBIGThF2AzZ4UqHw83LL3/KG28cIi8vNeJ5N70de+/eWkpKRvQYDeZfLtyUQ+EjsdnUhFZFfFBba+XnP/8Qp9MTVhafWGhMSommScrK8qNzUYqzgiHZkzt1qo2f/OR91q8/RFFRVtQXOdy7t5Z//vMQe/fWhl3Of96Ol74bOD0fptOpJrQqhj7NzXYee2wTTqebESPSwtonFhprbXVQVJTVY9CJQtGVIdWTk1Ly/vuV/PWvH5OcbKS4OLycdz0dL5gTu6RkBIDvNRT+5cJNORQuBoOgo8MV8X5nJVLqM4BDvVfEjI4OF48/vpmWFhujRwcGmfQUJBILjTU327nuumlqqDIWJLDGhoyR6+hw8ec/7+ajj6ooKMjEYulf1XpzYs+ZM7rXYyQnm5gzZ3TEab3CQQhU/r1w2PQIOJrholX6TZMS3l0Jydlw3iODXbuExuXy8OST26mqamHs2KyAz3rTl1c7vRGuxi66qAiz2cisWWqoMuokuMaGxHBlS4udxx77N9u2nWT8+Jx+G7hoB4qESjm0YEFBj2m9eqtjYWFW7wXPZqTUxbdztS46r/h2rta3R/g9KsJHSslzz+3l449ruk0TiLa+oHeN1dZaufLKyaSlmaN1iQo4KzQ26D25+voOfv3rTTQ02MjPT2fnzupendXB6OrojnagSH/SenVFd6DD+PH9G45NeITQW5egi27nav3v2Q+caXUqIqK11cHatXtZsaKkR1/3G28cZsOGI4wbl+N7xmMbiBVaY06nh8ZGGxdfPK5Px1X0wFmgsUHtyVVXt/HTn75Pc7Od0aMzwnZWB6PrvtEOFPEes6f34WK1usjPT1fL74SDvwi9JIj4BoNwsoEcOFDH2rV7KSwMzGQSy0AsL8E0dvp0O1deOZn0dNWLiwkJrrFB68lVVDTz2GObEEL6QpLDdVYHo+u+4QaK9CVrSX9pbrZ3+4FQhMA7fOLPuysTSoQDSW/ZQJqabPzud9vIy0vttnpHuEEiQNQ05nC4SUoyql5cLElwjQ2KkauqauHnP/8Qi8VITs6Z1QLCdVYHw3/fnpzYFRXN3HPPnH5nLekrUkpcLo1Zs0YNyPniGn//gHf4xPseEkaEA0lmZjJ33z0n6Gdut8aaNTtwOt1B110MJ0jkxIlWCgoyuOyyiVHRWHV1O9ddN1X14mLFWaCxAR+ubGqy8etff4TZbCAnJyUm5wjlxB45Mo3Tp61Rc5b3hZqadmbPzmfCBOWP6xUh9Agvf//ARav098nZcS++ocY///kZ+/fX9ZrNJJS+5s8vAGDr1lNR0Vhrq4OcnBSWLBnf52tS9MJZoLEBzV3pcLh59NEPOXWqLeppgYIhpaStzcmGDUdZsmQ8GRnmgLBkOOMsdzo9YWVoCEU4GR48Ho2qqlZ+8pNLQia2VQQhzufwxEPuyv3763j00Q8pLMzCZAqv7RtMX0BUNKZpkoqKJh588DxmzswPO2BG0UcSWGMDNlwppWTt2n0cO9Yc1RRdPSGEYMOGo3zyiR6Mcu21UykvnxAgQG9L1OtUB/o0ZBrO/tXV7VxwQVFiGzhXB3ScBmc7uDvAadVDkR1N+rpQHjsYLZCUAknpkJIHyVmQnAOZRfr2rnQVWxyJLx5obrbz+9/rfrhwDRwE1xcQFY2dOtXKwoVjmTFjJHAmYAYIOdyq6AcJrLEBM3Jbtpz0hSQHI9xlbCJZ7kZK6RvqWLJkfI/O8v4EvfjvF2p/p9ODlJKrr45s6ZEhjcsG1lPQfgoaD0LTZ2CrA2HonF8j9VdDEhiTwGACYQTpAamB5gaPs7OMAAlkjYORsyFnsm70jMoXEy2CaQfghRf2Yref8cNFosWu+gL6rTGbzYXRaGTFihLfedXyOYq+MiBGrrXVwTPP7GLUqIxuiytC+MvYRLLcjX/Za6+dipSS//qvHZw+bQ2ZtaSvQS/Qc9CMlJKqqha++MWZQR36cYOU0HYC6vfAqU26cfNmSDBawJwBGYV9bwVKDeyN8NlL+nujGSYug7EXgUlNt+gPjzzyLs3NdlatKvdpZ+XK9bhcHlpbHb7GZ1+06NXXm28e4eTJVk6ebOuzxqSU1NS0c/vtswJ89j0FzCgUPTEgRu7NNw/jcmlB107zz54ABAhjwYICXysy3HI9HfP0aSsjR6YFOMuBPmctCZcTJ1qZP7+Az38+DqcNaB5orYDa3XDyQ33oEQGWnP4ZtGAIQ+fQZWcmGLcdPl0LR1+DyTfA6IV6j1AREVJKmpvtrF69BYBVq8pZuXI9q1dvYdasfC64oBCDIXyNhSq3detJCgoymD9/dJ81VlPTzpQpeSxeXBijuxHHSKm7AOydw/+uDr1hiNRfpQQ6X6UGxmS94WnOAHOmriuDsbezJBwxDzypq7Py7W9vYNSo9G7zbrz4R2F5CZY9IdxyvZX1d4CbzUaEEGEvDdLa6vA52sNxgNfVWUlLM/P9718YlTBoq+Zho7WZS9KySevpgXW06svGT1kByZmhtwVDSmirghMfQPUmXUzCCJZcSBqEnqjLCh21kD4a5vwHpOYNfB36wVAIPPH23LyGDuCKKyaSl5ca4EKIhhaBPmmsrc1BR4eLr31tHr/85b959NElA+6/Dltf0D+N9YTLBu0noKNO92+3nwJrtf5ec3a6A6Dzv8BX33bR2QD1vnZuS82HzELIPQeGTYPU4X2r4xBjUANPXn31IEajCGng4Ex2kmDO6r6U661sMAd4uE7xYI72ULS2OvB4JCtXnhu1eT4brc38pvEEAFdlDAtd8OBaeOse/e+Zd4fe5o/HCXV74Njr0HxU7zVZ8iBlkIWQlKb76qw1sO0XMP8hSOnh2hXdEEKwalV5gJEbPjyNgoKMbuWiocVINeZ2a9TWdvDNby7kl7/8N88++wkA//M/1/bruiMlbH1B3zTWFW/uyNZK3add/4nuEtA/1BuXJovuDkgdrvu1+4rUwG3TNV69Wd+WXgAFiyCvRP87gQJOvMTUyJ040coHH1T2mog4kuwk4S53E2mQSbhOcX9He0+0tztparLx0EOLojpd4pK07IDXkExZEfgaahvokZCnNsGRf4CzTW+BZhYNvQc+LV83dFt/CfO/DSm5g12juMHbk/Nn585qioqyupWLhhYj0ZjXZ71s2RRmzszn0UeXAPheB5Kw9QWRacyfjnpoPgz1e6FhLzha0HtcncP1GWNjoz1h0BuMSZ1rAkqp6/3gi/o/SzaMv0o3egnkFojpcOU//3mQl1/+tEcj11P2hK6rAodTLpJjxoqWFjttbU6+8Y0FlJSMjNl5IkFqGsJgCHzv7oCK9VDxL70Xlzp8cIYjI8Varfcuz3ukfy3bAWKwhyv9hyofeGABX/vaXJYte4FPP63vk8aira/q6jaKirL55jfP63HEZ6gTVGOGzkhja41u1E5+AK1VINB7Z8lZuu9sKDQoXVY9Ojo1H6Z9EYZNHxr1CoNBG67cvr261yTEobInQKCzOtxykZaNNg0NHbhcGg89tIiJE4dGT2P3xgfB0Uxp+RqEwYB0OTj892vIaT9JXv4cvYcUT6H6aaOg5RjUbNODURQ9IoQgO9vCAw8s4De/uZRf/OLfXHxxMVlZyX3SWDT11dbmwGAwcM89c+LawHXTmKax97Uvk+JxMHHYpM5IZIOeRSQzygFb0cLby7M3w7Zfwog5MOMOPXAljolZT6693cn/+T+vU1iYFdZDH6t5cgOZjLm2th2j0ci3vnVet0UmBwupaXy8/m7K9j/N7ql3UDr3AWpf/xIjG/ZwOncGIyZfM+AJqqOCs033V1zwi6H5g+HHYPfkvEgp+eyzBn72sw98CRn6o7H+6stud1NT0863vnUe06b1bX7qUMBfYx+fcxszS++ieuP9jK7bQUPWJHInXIkwZwz55zQAKaH9JFiGwZwH9KCvIUxPGotZ7spjx5q8Jw+rfLjL2ESy3I03omvHjlM4HG5fWf9t0UBKyalTbaSlmfl//++CIWPgAITBQGn5GvZNWkHZgT8h/lrKyIY91OTNio6Bc7TCoZf114HEnKEPrbRWDOx545yXXjpAeroZIUS/NRZKX9C7xtxujZMnW7njjllxbeCgU2MXPcaRsZ+n9NM/I144n9F1O6gZVkbu1JsQyZn9M3CDoTEhIGMMuNphy8/16OY4JWZG7vjxliHRQwi2Rl1/1q3risvloaKimcLCLL773cWMGJHW72NGFY8LcXgd05ICpzuMnHhVdL6fyg16RFjlhv4fK1KkhOZjA3/eOOXQoUYOHqyPakKCUFrqSWOaJqmsbGHZsnPifz5c+ynY99+I9x9kfEZBwEcjJ10d/xpLHa5nJtq+SvfZxSEx88mZzcZ+Z/aPxnBjfyIpe6O52U5Tk41rr53KF74wOaK8fwOC9TTsWYNsOsTp2j3k+310+vA/GDnhykBHuff+RpKstXBJ4GsE9Pv7NRj1ybGKsHjrrSOkpib1eM8j/Q5CaamnSMrKymYuuKCIa66ZOiQawhEjJTQd0qfa1O4GgwmZms/po28knsYA0kZC63E9+vqcHqJGhygxM3IpKSb6Y+MiSeHVE8HSbfVn3TrQH5QTJ1rJyEjme9+7gMmTh9icLSmhegvs/RMSA6drPyG/4WNq8mYxcuJVnD78D/Lrd3FU8zBu0lLdUS4l2+xtFNZsJh8NisvPpOyqWK/P1Rl7UfdzJWfCpMjnMu22t+OUGvMsGb7vd5u9DbMwUGZJD+8gQhm5cGlpsbNjR3XAvLhoaCyUloJt16cKtFJams+tt5YGTfE35Gk6DAf+Rw98MqVCZiES4dNUwmnMS3oBHFsPYy4Y8v65rsSs62E2GzH08ej+aYMGc+23YNhsLo4da2LOnNH8+McXDz0D5+pAfvIUnt3/iSMpE1vqcKzJWRzKP4/kcZfTqnlIG3c5n+bNodmQzFZ7u+/h3+/o0FNp1WzRRecVX80WfXuU7ruUEqfU2O/oYJu9LeD8TqmF//0Ko54dQtEru3fXIKXEaNRFOdAa8zYMx4/P4atfnRt/kZS2Bvj4v2Dzj8BWD5nFeg9HGPSekcniM3BCCEZOuJKjuTNpNSSxzRHHGvNiMOkjJ1XvRaV+A0nMenIWi96TCzddFgSW9YYkb9ly0pdVoS9zcCI5f0+43RqnTrVhNhu56645LFpUGNOhlnDSC0kpadU81HtcnHY7qW2ppHDP7zHZG2lNzUd4POBph+GzkFJS6zzT65GjF1HvcdPs7OBA5/aJ5hSGT7gSTGZddDWd2THyF5xpdYaD26FPch1WAqbkbtuEKZl5Fr1Hsd/RoQsfmJac6mt1hnVMtPiKWIsR4ay19t57x8nKskRdY+HoSx+ibGXy5FweeOBcUlKGxkTjsFJ4ue1wfAMcXgcI3biJ7q33/HGXBgwFCoOBcZOWss3R3u0ZHznxqjjSmB8pw+HUv2HK8rjKgRkzIzdxYi4Gg2DPntO8/vphoPc1pLqm/Qk3hVckx4wUb1Z0p9PDpZdO4AtfmDwgizYGSy/kkZJqt5Mql53jLgen3A5cnS2yrPYqFu5bg1EYcKWPIT2M+5RmMNLcJfpti72NrFGLyW86TK6jAQGRiQ90oRzV1/5i5Jyg24QQzLNk+MQHhBZfqGN6nHr+vbOc3tZaa2y0cexYE4WFWezcWR1VjfWmL93AtTB9+gjuu28+FsvQmbzfYwovqcHpXXDgr3rarbTRvc4l7RaVajCEfsaLy88YOBi6GvPHlAcGxAoAACAASURBVAy20/r9iKO0ejF74tLSzCxYMIZNm6q48spJvQZ5SCkDnNWRpvAK5VztT5BJU5ONpiY7ZWX5rFhRwujRAzcp0ptWaHFqJsecdj5zdviGGgBShIFMgwmTEGQ2H2b63ifxGC04k7MJRypSSk65nQHbWjxuRpnMtLUcpTF3BhmuNsa1HiazYj0iEhEOKwl4lVIi/LZ5h0q22dsCdttmbwstwi7H1DOtA1k9p1c7G+htrbVjx5qC6iEaGutJX1JKKipamDUrn699bW6/RlJiQcgUXh218MnT0HBAjy7MLO7T8b1DhP74nvGKwBRrVKyPzNANhMaCXhTKyPlzxRWT2LSpirKyfJ8vIBj+DvA5c0aHtfab90vqzXnelyCT1lYHDQ0dFBRkcM89c5k6NW9Ao8AcmsYptwMNyVPNNXik1I2ZwUiSCPzKcur3MnX/07jMmbjCzEzgNXB1HhfDjUmMNpl977PaKki3VpOcMQb7iDL2ZI0np+kg4yvfJrXwc+GJ0JTsawkGOL9HzkFKyVZ7G/VuF3Uel2/4xOevIERr0++YgB5wkjpcX/LnLKe3tdaOH29h9+4a9u+vi4nGgulL0yTHjzezYMEY7rpr9pD0waUZjIE9OCnh9A7Ys0YfkswaF/C8a1Li7PR1uZFoEqRvBQCBAUg2GEgWAhPC90x3fcYLT7xLfv2uM0OUXp8chG/oBkJjwRBCT/IcR8TUyI0Zk8nnPz+e9euPUFycHTSaqj9rv0WyxlxvSClpbLTR2upk5Mg07rlnLvPnFwzotIAGt4s9jnZ22ttxSUmyEOQYTBhDXMPw09uY8ulfsVny8ESQc1IIgVEIn4ETQjDa1DkUI0yQMQaRNQGLECRnjKVVauwRRmZIjTQR/o+Vv/Mb8AntgKOD4cYkpprP+Ae8/gOz15HfG/YmKFqifHJhcPBgPVLKAdOY0+mhqqqFCy8s4rbbZg29qTXBcDv0JMXH/4WWOhKbKYUOj4tWzUOH5sEmNZydvSPv1QYL3RCd241Ai+ahKCmZsuT0gGcckyXQB1dcfmZ7hM9zTDUW9IToOTfjiJivJ+fxaDzzzG7ef/94j4Yu3DWsvFkW/NeqCneNuWC43Ro1Ne243RoTJ+aydOkUpk0b3mPPM5poUlLpcrDZ1kqFy44BQY7RSFIQ57Y/OfV7mb73v7CljsTTx1WzpZQI6dGHZ1JHIIUx5Bweu5QIATOS00j1czp3ODs4WbODgvw5pJq7G9qAqLJOvC1LIOj32yuaB9oq4fyfQObYPlz5wDKYab00TfK1r71GXl4Kb799LOYaa293Uldn5aabZnDppRPiYpqAs+0kHbv+E2drJadTRtIm9f6ZRA8/NwmBsbOnFq5x0KTEjW6AhNTI6zjN2OxiUkyWiOfJDYrGQtFaCYt+MuSmEQxagmYAo9HAbbeVIaXkww/1ZXe6GhBvKzKcNayg/wEqmqavlNza6sBkMrB4cSEXXzyOsWMzB2xY0iMlhxwdfGhrpc7tIsVgYKQxKazzp7VVMfXAn7CnDO+zgYPO+2mt1dexAoT3we1aByGwCIFd0/jEYaU0OR1L5/yQkzU7mHRyI4eASYWLg54jXOd32PfeWq3P14kDAzfYtLTYcbk8JCUZY66xujorbrfkwQfPY8aMobH6Rig6NA9VTjvVVe+Tc+C/8YgkOlJGkARYhAFDP38HDEJgRmAWBrT2UzS019BgMDM1u5AcY1JQjYViUDQWDM2jR1WmxlcatgHxBJtMBm6/fRapqUls2HAUs9nIyJHpvlZepA7wrs7zN98M3PfNN49w2WWB+3o8Go2NNqxWFwaDYPLkXG64YRqzZo2K2oKm4SClpMJl5y1rE40eN+kGI/mm8LNQJDlbmbZvDW5TKu5oLIvjfWD9H9wQrUyLwYBV81DhsnNOsn7ugvw5HOp8DbZvj873YILrrYXrcemRbxOW9uVqwz9PghFLjXnnwI0Ykcb99y+I6vqJ0aTN4+ao084+h5VKVwdTK15n0omNuFJHIkwpYUUk9wVD6ghSAZcll72ODs4xpzC8q6Hr4fkbcI2FwtkC2ZPCX95qiGhswMKdTCYDt9wyk0suGccrr3zKli0nSE1NIi8vlbfeOhp0bSoI7QD3Os9//euPsFpdzJ9fwGWXTeDNN4+wdetJTpxo4eabZ9DW5sThcGMwCGbOHMm5545h6tThAzINoCt1bicbrc0cddnJMBjJN3U3rj1lKZhlTmHywWcxuTqwpY2KTqUMpsChh5YKkG7ImnAmG0PLEd1Xl1VMqjBQ73Fh1TykGYykmlPPtC6r3tXnFXX6GqSUnD78D8wYmDbmgt6d3132D5oJwloN4y6H1Ly+X/OmR/QIsYtWnTnPuyv1ZVDOe6Tvxx2C6AEfodd/g75r7OTJVm6+uYSaGitz5oziK1+ZTVra0FqyyS0lx112dtnaOdKZOKDS0c78o+uY3rATa0YhmjBwyu3EKERQTfabTo0lAUJKDjceIbujiqSiz4eV8WTANRYKZxsUnBfeNQ8hjQ14TO+oURl8/evzuPzyibzyyqfs3VuLw+Fh2rQ8Fi4cAxC2A/zNN49gtboAcLs91NZaaWtzAOB06rP6580bzdy5o5k8edigTUJt1zx81NHCDns7ZiHIDzEs6XUiH3DqYvQ5kZ02pppTGF31NjkN+2nPiFFSWyl1A9emzx0ia4Ju4NpO6BnJO3uUQkKt28k4c0rgvt5MDgDF5YiK9XoUWd4sRvbm/A6yvy/qLH9B5+cd+gKTXkd9X6/R0Qw7V+vvL1qli2/napj9QML16MxmI5pGWOu/9aaxM6EW+qvT6aG52cFXvjKbRYsKh5T/rdXjZq/DyjZbGzapkSwMDDcmYdBcFB5+kdza7XyWUcSoTgPnjTSO9XJcJsAuNZqajjBCasGf81DnHwiNhTq3x6nPExzRS/Sl9zxDSGNRDTwJlnmht2wMdrub48ebOXCgnl27qqmqavUZfv+6GQwCj0eydetJDhyo922fNm04UsqAbV/+8kx+97srSE9PDisbRCSElSWhk9MuB7+oryLXlIRZCIYZk/AgOea0M85swRwkuMTbc/MaOoCp5hQW4WTutp9is+Qi7U368GLnsIHH48JmrSElLR9jf5et9/bcvIYOdAOXNQEP0ORxk2k0oklYkJKBy22nsXY3uSPKMJssgeHQAPkLkMXl4Tm/va3KLvtTXK6n7zr5PlzwGIzt7peI+Bq9ovMy+wFdjM42OLgWpqzQcwZGgWgGnkSqMSn1wJPc3BSSkgy9fg/BgsDmzy8AJFu3nvJtmzJlGHfeOYvrr5/GW28djZq+IDKN1bmdrGmq5u6cUQw3mal1O3nP2sx7HS2MSjKTZ0wiuVNnQnMz6eCzDD+9g88sI6jTziRC8EUa+wVi+Q/LRVNjNs3DsKaDTK5848zGzufcGeT3wemyDYzGPM7QGU/aqqD4MphyQ3gXOYQ0FtWeXLDMC71lY7BYTEyZkseUKXksW3YOVquTpiY7bW0O31Cjw+HBZnN1ptSazaJFz/j2f++9W0lNTSIt7ee+bX/+8zLfF9zb+SOlxywJfrR63DxcW8Fht50ij5mL0vX5XIcdNjZ3jp9PSe7uU/O2xPyN3DxLBoWfPY8UQjdwnYEi3mFGm7WG9JYjtAPp/Q3GEELvwfkbuc6hyya3ixNuB2NIxmIwIIHG2t3kV/6LGiC/YGHQTA6hghuCnjtYJgiAkx/o25sO9N/ICaGLzV+A3mGVg2vhrXv0bTPv7t95YkCkGhNCcOGFxbz99lHGjMns9llXggWBXXaZ3uvzN3Jr1lzJokVFPPXUzqjqC8LXGMCapmo2dDRj0zRmp6Rz0Gmj2u1kn7ODDIORgs4fa6F5mHjoRUbUbqc9o5DRQJ1fth/vVBr/QCz/YfxoakwCScNngL+R6xw+POaw8ZFNXzfO+/swIBoTInTGE68ffMwF4V/kENJYVI1c18wLUsqAbeEMBaSlmUOO60spWbkyMFPAj3/8AV1nrKxcuZ5Vq/QvvrdsEJESMkuCH4cdHfyjvYEiczJuqTE39cyPyzizJeC1K8GcyIcaD7GoZjPW9IIzCVz9AkVS0vJp73wNSbhOYG9Pzp+WI8isCeQY9cclx2jCJjU8SHJHlFED5I4oO9NK9CeSTA6h9s+bDuMvh+lf1lt/kVxPqPO8uzJw27srdRF6jz9laC4p0heNLV5cyL/+dTgs/QULUHnjjcPYbK6AbS+//CmLFxdFXV8Qnsa8dV2WMYxKlx2TEFS47Iw0JjHMaCJNGAI0NrbyTUZWb6I9oxAJ3bL9nHI7dUMXLBCL6GpMA8yndwZurFiPLC4P+H3wfl8DorHi8uBZhQDaT+irIEQSVTmENBazeXKPPPIuzc12n7HxGqjsbAuPPHJRxBX17r969RYeeGABq1aV841vrOeJJ/QWyf33z+fxxy/rVmYgM5U4pcZ71ma22drIMZpIiTCJqf9Q5VRziu6Ts7Uybf9TzLRWkZFe0Lfr6SWYxK8CgT44P59ce9oo0nIm+77L4y4HY5KSmZuScWZf//H9ruP9vYmwp/1HzIHl70By57n649T2H0bxDp90fR/lZyZW8+TC1ZiUkt/85iP276/rcdV6/6HKBQsKWLJkHOvWHWTfvjoAbr+9jKefvnpQNealyeNio7WZz5w2koUg22AKGfaf2XKEmbtXY00bjSaMIbP9+CdHiJgINNbeWsXUkxsZljMx4Dn3X8Ug6LI4sdRY1/29ASojyvRlduZ9Gz74VlxqLCaBJ1Lq89BWr9YN0KpV5QHC6ItzVwhBdrYlQFiPP17Oli36sMbjj1+GEIJVq/Threxsy4CKr97tYl1bPfUeF/kmc5/m2Qihz6vxGjghBBe7WxjTepiWtDFk9uV6wggm8T1wQvgynnjFKrMmYNU8tEtBS2dr95TbSbPmZoT0c9QL0b9MDsH2HzELPHY96ivZz5j2x6kthC5Uf7FdtEr/LDk7boJOItGYEIKvfnUuq1Z9xOHDjYwenYnZ3L0BJoQgKclASckIzjknj+rqdh54YAG///12kpONPP301YOqMdAbktttbXzY0YKB0EFcXkyuDqYc+G8cydlIgwkBIbP9GIXo2/VEoDE3kCQgO2cSFF+qa6y4nNNuJzUYqOwM/fdPCRZTjQXb3z9AxdUO12/QDVycaixmPTn/npeXaLT8gs0hgyjP6o+Qky4Ha1trMSLINp5pNzil1t2JHGRbV/zrX1C5gaKK1+hILwD0SeRNHjc5xjPpvnrdBr0Gk/jvG2wejre16yXbYOKqjFyMXa+hv3NjvOXba3TDNuc/ID2/e5lQTu1Ihiz7U88IiFVPLlKNOZ0e1q37lPfeq8Bm09eM8162wSCQEpKSDMyePYpzzx3ji0geChqTUnLMZefN9kZaPR6GmZJIEqJnjf3/9s48Oo76yvefqupd3VJrtTZblm0w8oZtbBMvAT9CzCMEEsLi5TkhkLDMYVjtIXljTjAMmUBOQjAh7z2WA8aEfYhJIDhkjB0IB8cQM05srJixAWFbtmzJ2pdeqn7vj1K3WlK31C11S+rW73OOT7uqq7pK1fXtW7977+9eq52ZnzxP4cmPwvqJdf6h5WhaguRprFMYTLE6TMPa5/ixqpb0u87J0thA+xsGfPIKNP6jZ12aaiylZb2EEKjqfeFlw/jRqLg2Usmnvk7+o/UULlXD3cc9edDXwV+6WvmSwxMOIkdbNxBzPvo5dn8zAZsZ16sPJX9Y7BRYrPGvEwKOvtPzweXng6JE3TcaQgj+5msPL5/jcDM70e7C8SAEtB2DrBJYcGfsAsxCwEMRBvbOsdtbLpVlvYaiMcMQ1Na2cvjwaYJBA7fbhstlxe22UVYWfZQ3mrQZOn9sO80//J1k99HZQBq7su1TLvrv58wpN4OUyQsRSw/J0FipxYZHtbDA6Y5atk8IwTPNdeHla3ImjM7vpRBmNmXRfNh5S8/6NNVYyubJRUsSiUwIyQSqu9p5ra0Br6pFjb9FSzIZLPEkEi3YgaftCO1ZPVlekckfca+LkUxCn2SSWERry1OvB5L/NC+EWZPSO818arTFMKIDBbUz5N6Kh6FqTFUVysuz+2VbjkVqAl281lqP3xCURHFNxtKYNdjB8s+20ukqitvAQXQtxVqfiMYEApuicqbNGdPAJVS1JJW0HTVbWDVW916fphpLSRXivkkihvEjbrvtXDZt2s0dd7xFIqPHsYgQgr2dbWxtqydPjZ1gYlNUpttdYTeKEKLXusGug6f1CKD0EqmmKBRYrL1cKQOug97xgfLzzdfWo9B8GA367Rt5XkIIjkUE5s+2Z+FVLdQEfHzY1Zq879IIQstnUDgHzrlzcAMXig/caZivH20y16f5vRUvma4xXQjebW/i+eY6LCgUWqLH3mJpbEnzQWyGn6Dm7LfPQETTUqz18WpMBRyqRoXVQX73KLCvxj6IiMFdkzOBGXYXB3wdydVYPLQdA3c5tNfC3v+TERpLyUguWpLIaAark81HXa281d5IoWYdtFtAiIHKdc2N4faz+ZsxE46HQZRkEnKmdr9n6fdUdiLoRxeiV5ZZh6HjUlRKLTaCCMqsNkqEbXgtOyLxtUBXPUy5FKZ9EwaabDvGgtqjRSZrrFkP8npbA0cCPoo0W8xWU30JaWyRzUX5ke102bypLdcVYhCNdQgDr6oxyWrvdZ6Ro7T6oPkQmdS2OIkghDmCc5fDgnWw56GM0VjKY3KjGaxOBYd8HbzSeoqCBAxc1KkBfZajXZf8U3uZXv1Mv6D5kIgjCByZYNI3vbpAs1JmsdEmDM6yOSmIs2PCoOfUXmv2p5r7T5A/I6l/z1gh1TG5TNLYiaCfl1pOEjQEeZol7r8lUmMXdBxl1SfP8IljwvCnBiRClHuyo7uk2GxHVth7E6uZapXNxSJnz2/BiH2X4TDBGTD/1h4vSoZoLKVN0+KehZ8mnAz62dragFeN08AFfVC3B0X3s9DhocrmpNrfyZaWk70NnO43OxIHfb12N9T4ygf5hUGNvwu/iBj1GUFoqzVfwbw5I9eFvougDxqqIegLp1MXalZO6QH+5msP/0iUWWz4hSBLUckfxMD5hcFBX0fv8+mL7oPmTyF/ptmfKhEDF/p7oi37WszOzr6WxD4vxHD3H2EySWOf+Tt5tqkOFYX8GO7JfkTRmL3uQ6p1o7eBE3pvPSTIUDQWNnCKge3Qa+BrCY/SQu7IZ5rrwgYv0sCZHzdMjcWDEYSWz6FgtpnoFRkmyBCNpUHL3rFBp6Hzaks9NkXBqcZ52UJlchr293JBhAiP4CK2i8SI1ncqCscDfhqNIMcDEckhHd3liTpODryu+VPoqDNfIWzoIgkt+xFU2pyDzgH8zN/Frs4WPvN39X9TCGivM//N/K755GiPPUE5YUIlgw6+ODr7S4bEIV8HL7WcIktV8SRSRKGvxuxuKpsP0WY1tRYewUW79xMgEY2Jxk9o6zyNS9XMEdyRt6F+H3yxHWDg34I4GVBj8eJvNUdwUy+DebeYc+XiIc00NuJdCMYig7l8DCF4q62RViNIUSK+/YgyOQNmT0WW04lwCfit2SjCGNRNUGK1QaD7NUS08kR91wlhZlGB+SpEzJJHeZoFr6rhjeMHKGYGqa8FOuvNUVvV/0pN09Phlgwa42W90pWBNHbY18F/tNaTo1rif4AM0Udj+1truTDQSp3TnFsZtVzXENxu8WrMcBbRrljIc+Yz3Z5l/sBOutB8c9KFYY0NN5MykSztfghhtqxSrbDgX6BwdmL7p5nGUhqTSwc2N52gzdC5Obc0nBDyq8Za3KrGd72mUKq72tnaVk+JNjS/fqyYnO3ouxQrBhOmmqV8EAJq3jJjVBOXgxDM++tP0Aw/QWuS56RFKUMkmg7TCnzqLO4Xk/OqFr6alUuWNoQ5VMEuaD8BzgLTuBXNHbO+/VSQyphcOjCQxi7z5PNU4wmyVDXhMniRhDR2qvlzbq9+HFd2hZl00lKDWxHhknQxy20lAb8w8AmDCquDcosd9eg7/Xq3ic//QK1Q+c+8s/vF5GJO/k4mesBMMMmrMosjO/NSd6wRZNRicmMdIQRths6rrfX8qrE2LL5XW+tpM3SEEASEwY6OJrxq/EHwvkQr17XQ4aFYMSg+9V8oNW/1GLjju81SVt1Pm8fKL8DedTrZf3hPGaLmw2HhK21H0YTeK1BfYrGRo1oo0KyJGzhDh5Yj0NVoGrcv/ztMmDeuDNx4ZyCNNetBftNSj0VhWAYOIjSmmn3jQm53tyJwtx9HibjPaT1q3v9JSoUXQtBu6Ahgjt3NJKvD/GENlcb6/K1w3UjlxAfYDB8zbK5evwUzuqdBpNTA+Vqg7QhMuxwWrs8YAzcY49pdqSgKN+eaE61fba3n1VazJ90VnoLwU+f+zjZaDX1IKciRJYfmOtz96glOmHopWGymYTveXZqp5Fyo6CmUWl80n4qa32P1txKweWKWHYoLI9jTKyuU4tx6tKcUkaecrJypuLrPTwhBh2Ewy+5imi2B+UZ6ADpOgDBg0ldg6qXJjbtJ0oZYGvuWO59Km4OPfR1MGEZ6f1+NuQI9nQsURSEr90xQtX73ediD0YdE9aULQYehk2excobNiU0PQN3fTDdqqC7kid09rW2Kz6Vw8kUUQK/fgpSO4HS/6Z6058Ci/w35Vak5zhhlXI/koLcIQ4QMXKeh805HM7nq0J4FPvN38Zeu1nBwOGomXEWfLtcVvSuJG5qNg2ddg9XfgiXQTqMe5GjQR6M+hCyxyEB55FyeEN3CD8+PEwY5moWpNmecmW6d0FJjJrJM+gp8+QGYsVYauHFONI1d4s7n7752CofZgLSvxvx2L0pE6y1lgPs8GvHqKzR68wmDqTYnM2zdE9Ijk8gUpX8X+27X5YhkxQrDnNzdcdJslfPlB8adgYNxPpIDwu6TSH7VWMvNuaV81NWGTxi9ii4nwqDB4ZCLMpKat/oZuhbvNA7MupEZ+/8fhTYvWOwDluGKSd/ge4wyRHQbeLuicpZ9kGxKIaDrtJmpZfPAGVdA+ZelYZOEiaaxh04fZZrVMaRuHZH01VjQmkXA6kYLdqFbHIPe532Jp8ydTxj4DUGRxcpkqwN7ZLJM3ySy4fR+Gw6dDeBvgZLFcOaV4CpI7fHGMOPayEXGB0IuytCy6M6CGpIx6SZUcijGwXticCEXZWgZ+hm6pryzug3dY5RZnPi0IfjTVYvZ7TgyNtGnbxxAR3YldkVlpj0r+nxAIUwB+ZoBYdaanHUdFMwauFqJZNwRTWM/bTjCH9ob0YXBIi17WKOYfhpTFE5OWETpsT/RoZUMeJ9HM3ShUl3R0Lsnd7sUlbMcLrJVrf+5W+xmV+2QgYvWuw1SZ+gCHaYnJXuSOT0n94zkHyPNGNdGTlEU3KrWKwYXcquoQKuhk62k6BIpiplFGRmDC7kutei9oZryqtg393YqD/8GT8vn+O3ecHeChI8dpQyRADpUB1mqFg6EhxEGdDVBoDv1ObsCplxiGjbXBJlMIolKNI3NsWfxD18H9mhGIgnUTVhE2ZG3UYSOSKCkXSQCFexnoDrPQChWBKABuSioikItUBtz726mL4fpAizd8eyZ/xOmdwJK/HPS4kUIEDo4VchzgWaHE0E4UT34vmmCw+GgvLwcqzWxB+lxP4UAos/h2e9r582203EHxePpExd1OyHwI3rWoQxuMIRBbuM/qDz8G5wdJwlqDvzBLoS71BytxUvEfCEhBO1CJ0e1UmV3YRGG2TDR32qKBwXypkPpErNKSaZmZvlazEmq01eBPTlV+sf7FALorbFfN9XRpAdwJ+AliVdfoW09B19i3snddLnNpqW9+ibC4BpzL8HtnUJegRerpmJXFCx6F6rFmVBXAxCAMsDycBBmBrPQTd3bvWDNSvD8RgFDN0McjjwzKSgOhBA0NDTQ2tpKZWVlv/dHpdVOOhEtCPyJvxN7AjdLKAAODNgnrt92isJnvs649u05QZXGvBk0ec8kr2E/hYd/i7duD5qvEeHMJ2jJImh1Yai2QcWsGAEUPUCX7qPM8DHJYkXzq6CqkD0ZypaZE8Vzp5kxt0wnVI0BzHlEkqQQqbE2oWNNcNJ3vPoKbfu3gnMpO32Awq4GfI58Grt7ugED9k0ECAqBZimiqCgPh6ZiVRSUQEe3ex7TkMRNX/0lwcAJ0V1KzDC9PvYCc7SYLt6UrtNmghqAqzCuXRRFIT8/n1OnTiV8uMwwcikoJPpFoAt3AqOieCsQDLfHXCRCtdBQOJeG3CqUyRfjchTg6aglr2EfWe21ODvNG0IoCqDQk3QmwhloXZqTVouTfE85k4pmoXmnmg1LXUWDP2WlUQHXuJEVT6KTxO/abxg4EjRyiWik0uYATzFfnP1PFP3Xw9h8TeTazESoWDF2IQQ+IQgisCoKDkXFY9FQQkYp5HK0JNa6Z/hEjPyE0W3cBFjdYMsGbfAH2TGHI6/3a5wM1bWd/kbu/Y3ga+ppCRHqN2b3wpKNQ/5Ypef2josBk0wG2S7efWNisSOKF9EOtHuncKJ0mbleGFgCHWh6F6oRRDMCCEUhaHES0BycwgKqyqXufM6Id5pAiBRd91HHni1HcH1J8nd9hs3Jx74OCizxG7pENBLaNmh3sf/sW5ix/zHcnSfRnEX9DEJQCHzCQABe1UKp1YZXtXBQ6fMLoKgJjuCSQChUYHEBAhQNjIBpaOMcAY1JVG1Ez3+MO28HQQhTfJHN/EINNX1Nw6poYFMUDIa+/5hAUQna3PicBXRmFdPmmUi7u5x2ex7HFCvFNgff95Zwpt2VmIFL4XWXjDFS8F3Pc3oIYKCPwH3S5pnI3vnraMsqxd12BEugg2D3HLc2Q0dHMMlq5xyHm1mOLPI067CnNQwbYZgTuI2AmS2p+yHLLDGIr9l8X2osbtJ7JKcoPc38Ptpk/oPezf6iIjr6NAAAFh5JREFU0G7o7Ghv4oIsL1kDdPUOCBG3Cz1aYDxqsDzoMyeK5s8y041HEH+gk6On9mPLq+Ir2YUscmYnXjUFhnzdJWlICjRWbLHxZaeXdzubKNCsgyaSQOzEk3g05rN5+WD2zbhP7qHqs9/i6mqgwF1Kri0Lj6ol1agZwkAPdKBZXahxx/QFGEZ3gpcwR402txky6Kw3J3PXf2xu6ioCz8SENfb5559TWVlJIBDAYknvn/1ESe+RHPQWYYhBfmh3tDfx0Omj7GhvirmNU1HRExjJ9a28EGtdrLY6qcYvDA41/IOSozv50rH/ZLErZ2gGLsQQrrskTUmBxpa6svm6O5/TepDTegBjkJFJVC3FWC8a9tNZ8zb1pw9SF/RTpwfQNCvTJl/AxAse5qyqlUzWO8hpP4baXtuvj+Nw0AMdWP0t6IGO2BuFEkd0nzlK0wOmC8+eY8bDPZPMYuZWl2nQIhmCgRvvpL+REwKx8/beq3bePuBw/oIsL3fmlXNBljfmNmfZXbQbetynUWlz8CWHp19CSd915M+CKV/vqYyQYnQhOBn002roXFZ8Nou8E1k8IwlJFSG3VSQhd5Yks0iBxhRFYY7DzXdyJjDN5qReD1AX9NOiB/ELg75Tm6JpSQhBudXGPHsWXs1iGrSgn5Pes7BWXMi8oll801PATbmlXO8tZrErhwlOL+qZ34Kv/BIW3gXl55tzP1tqoPWIOYVkiI1VATSri4AtG83q6pm7ZgS7jVm3UTOCZsKII990Q2ZPMos0OHLN+XMhIyaEeU6RtB4BIXjwwQcpKyvD4/Ewffp03n77bQzD4IEHHmDq1Knk5+dz9dVXc/q0Wdz9vPPOA8Dr9eJ2u9m1axeGYXD//fdTUVFBUVER3/nOd2huNjNIu7q6WLt2Lfn5+Xi9XhYuXEhdXR0ATz/9NFVVVXg8HqZMmcJjjz025Os1EqT3uFUIDrx1AzM+fhIx71aU//EwYuftKP/1CAf8Hcy46PGoTz1ZqsalnvwBP3qazcn29kZ0IeIa8cSdUBKqiJBihBA0GkH8QnCOw81iVw5uVYNFdwy+8+Af3hOXCbmtQssgR3SZRAo1BlBqtXOZ1c4FWToHfR0c9ndySg/QaATDkYJQfqFXs9Ck60Aw7GNxqRrzHR7yNQtFFhsTujvb2ydMjXq8MJrNrOOYXwVnrTaN3Mm9UL/frPfo0k2DFHkGSihDOZZxF6goqJrNjKehmD3bLFbzVbWax1XjaIYcMnChguqeieHlg/99mEcffZQPP/yQ0tJSPv/8c3Rd55FHHuG1117jnXfeobCwkFtvvZWbb76ZF154gXfffZfKykqamprC7sqnnnqKzZs3s3PnzrCR++d//meeffZZnnnmGZqbmzly5Ah2u529e/fidJqZpUVFRbzxxhtMmTKFd999l4svvpiFCxcyf/78gf+mUSKtjZwA2qwe/mPaGk6c/S/cDPzq7H+huLWeSVbPsKZd5mgWzna42dfVTuEg82rGEkIIWg2dNqEz1erkgiwvhcOo8h4VRTEz6yLjMiF3lt0rDVwGkUqNReJWNc5xejjHac7F9AuDFl2nXegYAgQCBQVNAQ2zikqWqmFJxr2mauCdYv4781umgak+YLoOhd4zGhNG9w5K9x+t9F5WNLOSiqrR0hrkxZcOsGr1bLJdQ4i9K4o5wTsyBtftutQsPnw+HwcOHKCwsJDJkycD8Nhjj/Hoo49SXl4OwMaNG5k0aRLPPvts1EM899xz3HnnnUyZYjZO/slPfsKsWbN4+umnsVqtNDQ0cOjQIebMmcM55/Q8mF9yySXh/59//vmsWLGCP//5z9LIpQJFUVh4wc/54PQxXm1r4NW2BgCuWPYgV+SVDbtk0GJnNtW+dtoM3RwFDUC7obOns5VznJ6YySypxBCCJiOITxiUWexc6ipgstWeuvYdSzb2nisVMnTSwGUUqdZYLGyKSoFFpQDzAfNU0M/jjce5Ibck+Q9tfVEUM/ljGIlhL768jxtv+j0oCjfcMETPTajObKTGPBOZdvYkHn74YTZu3MjHH3/MRRddxEMPPURNTQ2XX345asQcRE3Twm7GvtTW1lJRURFerqioIBgMUldXx7e//W2OHDnCqlWraGpqYu3atfz4xz/GarWybds27r33Xj755BMMw6Cjo4PZsxPsLj6CpH1MTlEUbs4r67Xu5iSJL0ezsCq7CJ8waBskPrens5XPgj72dLYOuF2yCQiDk0E/J/UAk60OvpNTzLdzJlBpc6TOwIXo+/nSwGUkqdRYvDzeeJztHU083nh8xI45HFatmsVjj32dVauGGXuPobE1a9bw3nvvUVNTg6Io/OAHP2DixIls27aNpqam8L+uri7KyqJ/V6WlpdTU1ISXv/jiCywWCxMmTMBqtXLPPfdw4MAB3n//fd544w22bNmCz+fjiiuuYP369dTV1dHU1MTXvva1fjHUsUTaG7lYrXKSddFLrHZWZxfRZQxs6M5xeqi02MPullRiCEGLHuREd0LJuc5sbsgt4VvZhZSlcvQmGZekWmPxcENuCRe6vNyQWzJixxwO2dl2brjhHLKzkz9N6ODBg+zYsQOfz4fD4cDpdKJpGjfddBMbNmwIG65Tp07x29/+FoDCwkJUVeXTTz8Nf87q1av5xS9+wWeffUZbWxv/+q//ysqVK7FYLOzcuZN9+/ah6zrZ2dlYrVY0TcPv9+Pz+SgsLMRisbBt2zb++Mc/Jv1vTCZp7a4cqFUO9DQ/HS6lVjurc4r4TWs9J4J+8jRLr7k6QgiyVI3zujPJ+hZ8Tga6ELR0N2lUgAqrgzn2LKbZnXHNM5JIhsJIaWywcyi02NhQWBFeHs8Pcj6fjx/+8IdUV1djtVpZsmQJjz/+OMXFxQghWLFiBbW1tRQVFbFy5Uq+8Y1v4HK52LBhA0uXLiUQCPCHP/yB6667jtraWs477zy6urq46KKL+OUvfwnAiRMnuOmmmzh69Chut5uVK1eydu1aLBYLjzzyCFdffTU+n49LL72Uyy67bJSvyMCkfReCzU0naDP0sNhConSrGt/1Fif1WH5h8Peudt7taCIgBHmalY997fiFEW5fL4Tgw65WbIrKXId7yMcSQhDATCIJCoGqKJxhc1JlczHJasc5CnE/ydBI9y4EI6mxsXLs6upqqqrGXxftsU6s7yWjuxB811vc68ku1BMuFU96NkVlgdPDTLuLPZ1t/KWzhSY9SE3Qh18YLHZks8fXRrW/kyqbM+4nzpBB6zQMOrtHamCmYc+wuZhud1FutcsRm2RUGEmNRSKEoM3Qe40aI0eV431EJ4mPUTNyLS0+XnxxP6tWzRq23zpaq5wQ8ZTwShSnqrEsK4fFrmzqgn7+7+la3utq4XDArPpfYbEzyWLnlB4w580oZsFnA0Gwu+O4aa4UzORoc77PJKuDSVY7hRYr+ZoVl6ImVcSpuBaSsUky9QWxNZbKeyqyifGrrfVhYxfZgHWsoXdP4fGo2vAqCkmSxqgZuRdf3M+NN74BMPQU2zgIlRcC4pqcmgiaolBqtXNf0WQu+OLv4fV3F0yiUwjaulP6fcI0bk5Vxa1qOBUNh6pgV1QcikqWqiXdoEUjlddCMrbIBH1Bj6ELGTgYmTjgUGk1dOqCfrDY8CbQFFaSOkbtWwil1g47xXYQQmWFBirhNRyiZZ79ob0xJUKM1sE8kWOk+lpIxg6ZrK9fNdaOSX0BeFQNLDbzVTImGLUgTypTbCMJlRdKhXuub+bZjklzuMJTwKut9UlPsd7cdKLXZ4aOvbnpRNyfkcprIRlbSH0lRl99AZzSA9QHAwl9jqYoeDWLdFWOIeR4ehgoilleKDJGEIohuFUtaU+aMgAvGY+Mpr5ajSCqHiRXs0h9pTnSyA2Tkcg8G0oAXiaZSDKB0dLXPQZM1iwUataYx5JJJumBzElPAgNldybzGCEhhhhI7PH0zJNI0oHR0tdABg56kkxaE2jJJRl5pJFLExItrRRPzzyJRGISTV+n9MCAcT+PqjEhg5NM3G53rzJgQ2Hy5Mls3749SWc0NKSRSwOGEoCXSSYSSXxE05dLVWnUgwMaukxPMmlrawu34UlnZEwuDRipALxEMh6Jpi+PaiFXs6CiZKy+gsFguIHqWCOZ5yZHcmnCd73FvWJwIUOX6tqBEsl4oK++wIzJFYzBhskPPPAAV155Za91t912G7feeivNzc1873vfo6SkhLKyMu6++2503YwZbt68maVLl3LHHXeQl5fHxo0bOXToEOeffz45OTkUFBSwcuXK8GcqisKhQ4cA6OzsZN26dVRUVJCTk8OyZcvo7OwE4He/+x0zZ87E6/WyfPlyqquro563z+fj9ttvp7S0lNLSUm6//XZ8Ph8Af/rTnygvL+fBBx+kuLiYa6+9NmnXa2yacUlURiIAL5GMV9JFX6tXr+a+++6jpaWF7OxsdF3n5ZdfZuvWrVxzzTVMmDCBQ4cO0d7ezte//nUmTpzIjTfeCMDu3btZtWoVJ0+eJBAIcN1117FixQp27tyJ3+8nVnHw9evX8/HHH/P+++9TXFzM7t27UVWVTz75hNWrV/Paa6+xfPlyfvGLX3DppZdy4MABbLbezW1//OMf85e//IW9e/eiKArf+MY3uP/++/m3f/s3wOx8cPr0aWpqajAMI9ppDImMHsn19aWP5cZ+Ekm6IfUVm3ZD5/XWBtpTkHlZUVHB/Pnzee211wDYsWMHLpeLyspKtm3bxsMPP0xWVhZFRUXccccdvPjii+F9S0tLueWWW7BYLDidTqxWKzU1NdTW1uJwOFi2bFm/4xmGwVNPPcWmTZsoKytD0zSWLFmC3W7npZde4pJLLuGrX/0qVquV9evX09nZyfvvv9/vc5577jl+9KMfUVRURGFhIffccw/PPvts+H1VVbn33nux2+04nc6kXa+MNXLJqBAikUiiI/U1MKmewrNmzRpeeOEFAJ5//nnWrFlDTU0NgUCAkpISvF4vXq+XG2+8kZMnT4b3mzhxYq/P+elPf4oQgkWLFjFz5kyeeuqpfseqr6+nq6uLqVOn9nuvtraWioqK8LKqqkycOJFjx44Num1FRQW1tT0ZrYWFhTgcjgSuQnxkpLtSVgiRSFKH1NfgpLqm51VXXcW6des4evQoW7duZdeuXXi9Xux2O/X19TGTNvp+L8XFxTzxxBMAvPfee1x44YWcd955TJs2LbxNQUEBDoeDw4cPc/bZZ/fav7S0lH379oWXhRAcOXKEsrKyfscuLS2lpqaGmTNnAvDFF19QWtozNzFV90xGjuRCSRmhNPsLvvh7r87G412AEslwkPoanFRP4SksLGT58uVce+21VFZWUlVVRUlJCStWrGDdunW0tLRgGAaHDx/mnXfeifk5r7zyCkePml0kcnNzURQFTet9zqqqct1113HnnXdSW1uLruvs2rULn8/H1Vdfze9//3vefvttAoEAP//5z7Hb7SxZsqTfsVavXs3999/PqVOnqK+v57777mPt2rXJvTBRyEgjB4lXCJFIJPEj9TX6rFmzhu3bt7NmzZrwui1btuD3+5kxYwa5ublceeWVHD9+POZnfPjhh5x77rm43W4uu+wyNm3aRGVlZb/tfvaznzF79mwWLlxIXl4eP/jBDzAMg+nTp/PrX/+aW265hYKCAl5//XVef/31fkknAHfffTcLFixgzpw5zJ49m/nz53P33Xcn52IMgJJIsHjBggUiVvbNWCNygmcI+aQpGQ0URdkjhFgQz7bporFM11d1dTVVVVWjfRqSPsT6XgbSWEaO5EayRYdEMt6Q+pKkExmZeCIrhEgkqUPqS5JOZKSRg5Fp0SGRjFekviTpQka6K0OkSwUDiSQdkfqSpAMZbeQkEolEMr6RRk4ikUgkGYs0chKJRCLJWKSRk0gkEknGIo2cRCKRZAgXX3wxzzzzzIjsP9xjjRQZO4VAIpFIxhvbtm0bsf2He6yRQo7kJBKJZBwQDAZH+xRGBWnkJBKJJI144IEHuPLKK3utu+2227j11ltZvnw5Tz75JACbN29m6dKl3HHHHeTl5bFx40Z0XWfdunUUFBRQWVnJo48+iqIoYQPYd/9ly5axfv16cnNzw01ZQ0RuC/DEE09QVVWFx+NhxowZfPTRR+HznTp1anj91q1bU3p9+iKNnEQikaQRq1ev5s0336SlpQUAXdd5+eWXe3UjCLF7926mTJnCyZMn2bBhA0888QTbtm1j7969fPTRR+Hu4rHYvXs306dPp76+nrvuuovvfe97UWuTvvLKK2zcuJEtW7bQ0tLC7373O/Lz8wGYOnUqf/7zn2lubuaee+5h7dq1A3ZGSDbSyEkkEkmy8bXA3x83X5NMRUUF8+fPDxuoHTt24HK5+NKXvtRv29LSUm655RYsFgtOp5OXX36Z2267jfLycnJzc/nhD3846LGuv/56NE3jmmuu4fjx49TV1fXb7sknn+Suu+5i4cKFKIrCtGnTwl3Ar7rqKkpLS1FVlZUrV3LGGWfwwQcfJOFKxIc0chKJRJJsDr4I/3mj+ZoC1qxZwwsvvADA888/H3UUBzBx4sRey7W1tb3W9X2/L8XFxeH/u1wuANra2vptd+TIEaZOnRr1M7Zs2cLcuXPxer14vV72799PfX191G1TgcyulEgkkmQzfVXv1yRz1VVXsW7dOo4ePcrWrVvZtWtX1O361hMtKSkJdwIH0zglg4kTJ3L48OF+62tqarj++ut5++23Wbx4MZqmMXfu3BFtxyRHchKJRJJs7Nkw5wbzNQUUFhayfPlyrr32WiorK+Nu8Hr11VezadMmjh07RlNTEw8++GBSzuf73/8+P/vZz9izZw9CCA4dOkRNTQ3t7e0oikJhYSEATz/9NPv370/KMeNFGjmJRCJJQ9asWcP27dtjuiqjcf3117NixQrmzJnDvHnz+NrXvobFYkHTtGGdy1VXXcWGDRtYs2YNHo+Hb37zm5w+fZoZM2awbt06Fi9ezIQJE9i3bx9Lly4d1rESRUlk2LhgwQLx17/+NYWnI5FkHoqi7BFCLIhnW6mxsUF1dXXco6N0Ztu2bdx0003U1NSM9qnERazvZSCNyZGcRCKRjBM6Ozt58803CQaDHDt2jHvvvZfLL798tE8rpUgjJ5FIJOMEIQT33HMPubm5zJs3j6qqKu67777RPq2UIrMrJRKJZJzgcrn48MMPR/s0RhQ5kpNIJBJJxiKNnEQikUTBMIzRPgVJBEOdWyeNnEQikfQhKyuLY8eO4ff7R3TisiQ6QggaGhpwOBwJ7ytjchKJRNKH8vJy6uvrqampGbctasYaDoeD8vLyhPeTRk4ikUj6oKoqRUVFFBUVjfapSIaJdFdKJBKJJGORRk4ikUgkGYs0chKJRCLJWKSRk0gkEknGklCBZkVRTgHpUclTIhk7VAghCuPZUGpMIhkSMTWWkJGTSCQSiSSdkO5KiUQikWQs0shJJBKJJGORRk4ikUgkGYs0chKJRCLJWKSRk0gkEknGIo2cRCKRSDIWaeQkEolEkrFIIyeRSCSSjEUaOYlEIpFkLP8fSttMqGqxIGwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Author: Ron Weiss <ronweiss@gmail.com>, Gael Varoquaux\n",
    "# Modified by Thierry Guillemot <thierry.guillemot.work@gmail.com>\n",
    "# License: BSD 3 clause\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "print(__doc__)\n",
    "\n",
    "colors = ['navy', 'turquoise', 'darkorange']\n",
    "\n",
    "\n",
    "def make_ellipses(gmm, ax):\n",
    "    for n, color in enumerate(colors):\n",
    "        if gmm.covariance_type == 'full':\n",
    "            covariances = gmm.covariances_[n][:2, :2]\n",
    "        elif gmm.covariance_type == 'tied':\n",
    "            covariances = gmm.covariances_[:2, :2]\n",
    "        elif gmm.covariance_type == 'diag':\n",
    "            covariances = np.diag(gmm.covariances_[n][:2])\n",
    "        elif gmm.covariance_type == 'spherical':\n",
    "            covariances = np.eye(gmm.means_.shape[1]) * gmm.covariances_[n]\n",
    "        v, w = np.linalg.eigh(covariances)\n",
    "        u = w[0] / np.linalg.norm(w[0])\n",
    "        angle = np.arctan2(u[1], u[0])\n",
    "        angle = 180 * angle / np.pi  # convert to degrees\n",
    "        v = 2. * np.sqrt(2.) * np.sqrt(v)\n",
    "        ell = mpl.patches.Ellipse(gmm.means_[n, :2], v[0], v[1],\n",
    "                                  180 + angle, color=color)\n",
    "        ell.set_clip_box(ax.bbox)\n",
    "        ell.set_alpha(0.5)\n",
    "        ax.add_artist(ell)\n",
    "        ax.set_aspect('equal', 'datalim')\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# Break up the dataset into non-overlapping training (75%) and testing\n",
    "# (25%) sets.\n",
    "skf = StratifiedKFold(n_splits=4)\n",
    "# Only take the first fold.\n",
    "train_index, test_index = next(iter(skf.split(iris.data, iris.target)))\n",
    "\n",
    "\n",
    "X_train = iris.data[train_index]\n",
    "y_train = iris.target[train_index]\n",
    "X_test = iris.data[test_index]\n",
    "y_test = iris.target[test_index]\n",
    "\n",
    "n_classes = len(np.unique(y_train))\n",
    "\n",
    "# Try GMMs using different types of covariances.\n",
    "estimators = {cov_type: GaussianMixture(n_components=n_classes,\n",
    "              covariance_type=cov_type, max_iter=20, random_state=0)\n",
    "              for cov_type in ['spherical', 'diag', 'tied', 'full']}\n",
    "\n",
    "n_estimators = len(estimators)\n",
    "\n",
    "plt.figure(figsize=(3 * n_estimators // 2, 6))\n",
    "plt.subplots_adjust(bottom=.01, top=0.95, hspace=.15, wspace=.05,\n",
    "                    left=.01, right=.99)\n",
    "\n",
    "\n",
    "for index, (name, estimator) in enumerate(estimators.items()):\n",
    "    # Since we have class labels for the training data, we can\n",
    "    # initialize the GMM parameters in a supervised manner.\n",
    "    estimator.means_init = np.array([X_train[y_train == i].mean(axis=0)\n",
    "                                    for i in range(n_classes)])\n",
    "\n",
    "    # Train the other parameters using the EM algorithm.\n",
    "    estimator.fit(X_train)\n",
    "\n",
    "    h = plt.subplot(2, n_estimators // 2, index + 1)\n",
    "    make_ellipses(estimator, h)\n",
    "\n",
    "    for n, color in enumerate(colors):\n",
    "        data = iris.data[iris.target == n]\n",
    "        plt.scatter(data[:, 0], data[:, 1], s=0.8, color=color,\n",
    "                    label=iris.target_names[n])\n",
    "    # Plot the test data with crosses\n",
    "    for n, color in enumerate(colors):\n",
    "        data = X_test[y_test == n]\n",
    "        plt.scatter(data[:, 0], data[:, 1], marker='x', color=color)\n",
    "\n",
    "    y_train_pred = estimator.predict(X_train)\n",
    "    train_accuracy = np.mean(y_train_pred.ravel() == y_train.ravel()) * 100\n",
    "    plt.text(0.05, 0.9, 'Train accuracy: %.1f' % train_accuracy,\n",
    "             transform=h.transAxes)\n",
    "\n",
    "    y_test_pred = estimator.predict(X_test)\n",
    "    test_accuracy = np.mean(y_test_pred.ravel() == y_test.ravel()) * 100\n",
    "    plt.text(0.05, 0.8, 'Test accuracy: %.1f' % test_accuracy,\n",
    "             transform=h.transAxes)\n",
    "\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())\n",
    "    plt.title(name)\n",
    "\n",
    "plt.legend(scatterpoints=1, loc='lower right', prop=dict(size=12))\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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